Methods of mast cell tumor prognosis and uses thereof

ABSTRACT

The present disclosure provides methods for detecting a high-risk phenotype of a mast cell tumor (MCT) in a biological sample from a mammal, preferably a dog. Kits, PCR probes and primers to detect high-risk MCT are also provided.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of 62/343,503 filed May 31, 2016, Matthew Breen, entitled Methods of Mast Cell Tumor Prognosis and Uses Thereof with Atty. Dky. No. 127-97-PROV which is hereby incorporated by reference in its entirety.

1. FIELD

The present disclosure provides methods for detecting a high-risk phenotype of a mast cell tumor (MCT) in a biological sample from a mammal, preferably a dog. Kits, PCR probes and primers to detect high-risk MCT are also provided.

2. BACKGROUND

2.1. Introduction

Mast cell tumors (MCTs) are a common form of skin tumor in the domestic dog, accounting for up to 20% of all skin cancers in this species (Finnie and Bostock, 1979, Rothwell et al., 1987, Villamil et al., 2011). The clinical behavior of MCTs varies widely from benign tumors, which can be cured by surgical removal alone, to highly malignant tumors that exhibit aggressive biologic behavior and high rates of metastasis, even in dogs treated with a combination of surgery, radiation therapy and chemotherapy. This variable biological behavior of MCTs poses a clinical challenge to veterinary clinicians.

Treatment decisions are made based on the presence or absence of prognostic factors, such as histological grading, clinical stage, and expression of cell proliferation markers. Among many prognostic indicators, histopathological grading of tumors with Patnaik's 3-tier (grades 1-3, with grade 3 being the most malignant) and Kiupel's 2-tier grading (either low or high grade) schemes has been widely used for the prognostication and treatment decision (Patnaik et al., 1984, Kiupel et al., 2011). These schemes are based on consideration of histological features such as cellular morphology, mitotic index and extent of tissue involvement. These histological features, however, can be subjective, leading to inter-observer difference in grading. In addition, histopathological grading requires a tumor biopsy, which may involve general anesthesia and costly and potentially invasive surgical procedures. The need for a reliable, non-invasive prognostic test for canine MCTs remains paramount.

Numerical and structural chromosomal abnormalities are hallmarks of cancer. Such changes to the genome have been utilized widely as diagnostics and prognostics in a range of human cancers (Mitelman et al., 2007, Frohling and Dohner, 2008, Hanahan and Weinberg, 2011). Recent advancement in genomics technology now allows us to analyze the genetic abnormalities in the dog at a genome-wide level (Breen, 2009). DNA copy number changes correlated with prognosis have a potential to offer a molecular means of predicting outcome as well as identification of potential therapeutic targets. Genome-wide copy number analysis has thus far not been performed in canine MCTs, with the exception of a single MCT cell line (Lin et al., 2009).

3. SUMMARY OF THE DISCLOSURE

This disclosure is directed to a method for detecting a high-risk phenotype of a mast cell tumor (MCT) in a biological sample by enumeration of certain regions of certain dog chromosome (CFA) from a dog which comprises: (a) measuring copy numbers of regions of dog chromosome (CFA) CFA5:38, CFA 20:32, CFA 20:46, and CFA 31:18, in the biological sample; (b) comparing the measured copy numbers to those of appropriate canine normal controls; and (c) if the copy numbers of regions of CFA 31:18 and CFA 20:46 are increased and the copy numbers of regions of CFA5:38 and CFA 20:32 are reduced from that of the appropriate controls, detecting that the dog has increased likelihood of a high-risk phenotype of a mast cell tumor (MCT).

In one embodiment of the method of par. [0006], the copy number increases are >1.5.

In another embodiment of the method of par. [0006] or [0007], the copy number reductions are <0.5. In yet another embodiment, both the copy number increases are >1.5 and the copy number reductions are <0.5.

In the methods of par. [0006]-[0008], the copy numbers may be measured by fluorescence in situ hybridization (FISH); polymerase chain reaction (PCR), such as digital droplet PCR; comparative genomic hybridization (CGH); or next generation sequencing.

In the methods of par. [0006]-[0009], the biological sample may be a tissue sample such as a fresh-frozen sample or a fresh sample or a fixed sample such as a formalin-fixed, paraffin-embedded (FFPE) sample.

The invention also provides a kit for detecting a high-risk phenotype of a mast cell tumor (MCT) in a biological sample in a dog comprising: (a) at least a plurality of reagents selected from the group consisting of: nucleic acid probes and/or primers capable of specifically detecting CFA5:38, CFA 20:32, CFA 20:46, and CFA 31:18; and (b) instructions for use in measuring a copy number of regions of CFA5:38, CFA 20:32, and CFA 20:46, and CFA 31:18 in a biological sample from a dog, wherein if the copy numbers of regions of CFA 20:46 and CFA 31:18 are increased and the copy numbers of regions of CFA5:38 and CFA 20:32 are reduced from that of measured copy numbers for appropriate controls, and detecting that the dog has increased likelihood of a high-risk phenotype of a mast cell tumor (MCT).

In the kit of par. [0011], the reagents may comprise primers with SEQ ID NOS:4-7 and 11-14 and probes with SEQ ID NOS:15-18.

The disclosure also provides a method for treating a dog with a mast cell tumor (MCT) which comprises: (a) measuring copy numbers of regions of CFA5:38, CFA 20:32, CFA 20:46, and CFA 31:18, in a biological sample from the dog; (b) comparing the measured copy numbers to those of appropriate canine normal controls; (c) if the copy numbers of regions of CFA 20:46 and CFA 31:18 are increased and the copy numbers of regions of CFA5:38 and CFA 20:32 are reduced from that of the appropriate controls, detecting that the dog has increased likelihood of a high-risk phenotype of a mast cell tumor (MCT); and (d) treating the dog with a chemotherapy regimen.

In the method of treatment, the chemotherapy regimen may comprise a treatment with an alkylating agent, a tyrosine kinase inhibitor, a vinca alkaloid or a combination thereof. The alkylating agent may be a nitrosourea such as lomustine. The tyrosine kinase inhibitor may be toceranib, masatinib, or imatinib. The vinca alkyloid may be vinblastine.

4. BRIEF DESCRIPTION OF THE FIGURES

FIG. 1: Representative oaCGH profiles of four canine MCT cases (Cases, 68, 38, 54 and 48) from the cohort used in the present study. In each case, the oaCGH profiles present the log₂ tumor/reference ratio on the y-axis and the genome coordinates of the ˜180,000 probes on the custom array positioned by chromosome location across all 38 canine autosomes and X chromosomes on the x-axis. Histological grading and KIT mutational statuses were as follows: case 68, Grade 1/low grade (3-tier/2-tier grading system) tumor without KIT mutation; case 38, Grade 2/low grade tumor with KIT Exon 11 ITD mutation; case 54, Grade3/high-grade tumor without KIT mutation; case 48, Grade 3/high-grade tumor with KIT Exon 11 ITD mutation.

FIGS. 2A and 2B: Genomic imbalance in MCTs grouped by 3-tier (left) and 2-tier (right) histological grading. (2A) Numbers of copy number aberrations CNAs and (2B) total (combined) length of all CNAs was compared between groups stratified by histological grading.

FIGS. 3A and 3B: Penetrance plots of genome-wide CNAs in canine MCTs, stratified by (3A) 3-tier and (3B) 2-tier grading schemes. Genomic locations are plotted along the x-axis. The y-axis indicates the percentage of corresponding cohort that demonstrated either copy number gain (shown in darker gray above the midline) or loss (shown in lighter gray below the midline) of the corresponding chromosome region. CNAs were more frequent in higher grade tumors.

FIGS. 4A and 4B: Genomic imbalance in MCTs with wild-type KIT and tumors with mutant KIT. (4A) Numbers of CNAs and total CNA length was compared in tumors with and without KIT mutations. (4B) Penetrance plots of genome-wide CNAs in canine MCTs, stratified by KIT mutational status. Genomic locations are plotted along the x-axis. The y-axis indicates the percentage of corresponding cohort that demonstrated either copy number gain (shown in darker gray above the midline) or loss (shown in lighter gray below the midline) of the corresponding chromosome region. Canine MCTs without KIT mutations exhibited limited numbers of CNAs except for CFA 20. Canine MCTs with KIT mutations exhibited a wide range of CNAs with high frequencies, including gains of CFA 4, 13, 31, 36 and 38 and loss of 5, 16 and 28.

FIGS. 5A and 5B: GISTIC analysis of CGH profiles of MCTs with wild-type KIT and tumors with mutant KIT. The peak region of significance is highlighted in dark grey, flanked by a broader region of reduced significance shown in pale grey. Genomic locations are plotted along the x-axis, and G-scores for statistical significance are shown on the y-axis.

FIGS. 6A and 6B: Comparison analysis of penetrance plots of CNAs in high-risk and low-risk MCTs. The y-axis indicates the percentage of difference of CNA frequencies (darker gray: gain, lighter gray: loss) between two groups. Regions where CNA frequencies are significantly different between two groups are shown in darker gray (gain) or lighter gray (loss) above chromosome position. (6A) Comparison of KIT mutant tumors and low-risk tumors. (6B) Comparison of high-grade tumors and low-risk tumors.

FIG. 7: Performance of the ddPCR assays. Upper two panels: correlation of ratios estimated with oaCGH and ddPCR (x-axis, ddPCR-based ratio; y-axis, oaCGH-based ratio). Lower three panels. Difference between low-risk and high-risk MCTs in CFA 20^(46Mb/31Mb) ratio, CFA 31/CFA 5 ratio and CNA^(MCT) score. The broken line indicates the CNA^(MCT) score cut-off value of 1.27, the threshold valued identified to separate low-risk and high-risk MCTs.

FIGS. 8A and 8B: Genomic imbalance in CFA 20 in MCTs with wild-type KIT gene, stratified by (8A) 3-tier and (8B) 2-tier histological grading. Frequency of loss of a middle segment of CFA 20 (15-43 Mb) and gain of a distal segment of CFA 20 (45-50 Mb) increased as histological grade increased.

FIGS. 9A and 9B: Genomic imbalance in MCTs with exon 11 ITD mutations and tumors with other types of KIT mutation. (9A) Numbers of CNAs and total CNA length was compared in mutant KIT tumors by their mutational type. (9B) Penetrance plots of genome-wide CNAs in canine MCTs, stratified by KIT mutational type. Genomic locations are plotted along the x-axis. The y-axis indicates the percentage of corresponding cohort that demonstrated either copy number gain (shown in darker gray above the midline) or loss (shown in lighter gray below the midline) of the corresponding chromosome region.

FIGS. 10A and 10B: Penetrance plots of genome-wide CNAs in MCTs with KIT mutations, stratified by (10A) 3-tier and (10B) 2-tier grading schemes. Genomic locations are plotted along the x-axis. The y-axis indicates the percentage of corresponding cohort that demonstrated either copy number gain (shown in darker gray above the midline) or loss (shown in lighter gray below the midline) of the corresponding chromosome region.

FIG. 11: The MCT^(CNA) score in a validation cohort consisting of 38 MCT cases. High-risk MCTs exhibited significantly increased MCT^(CNA) score compared to low-risk tumors. The broken line indicates the MCT^(CNA) score cut-off value of 1.26, the threshold value used to separate low-risk and high-risk MCTs. Box plots indicate 25th to 75th percentiles with whiskers indicating the minimum and maximum values and with dots showing outliers

5. DETAILED DESCRIPTION OF THE DISCLOSURE

This disclosure provides a method for genomic profiling of canine mast cell tumors that identifies DNA copy number aberrations (CNAs) associated with aggressive tumor phenotype

Briefly, canine mast cell tumor (MCT) is the most common skin malignancy in dogs and presents with heterogeneous biological behaviors, posing a clinical challenge to veterinary clinicians. Knowledge regarding the underlying molecular aberrations in the development and progression of MCTs are largely unknown. Characterization of genomic alterations in the tumors may identify genome regions and/or genes responsible for the malignant alteration of canine MCTs, facilitating the development of new therapeutic strategies and improved clinical management of this cancer. We performed genome-wide DNA copy number analysis of 109 canine primary MCTs using oligo array comparative genomic hybridization (oaCGH). We demonstrated a stepwise accumulation of numerical CNAs as tumor grade increases. Tumors with KIT mutations showed genome-wide aberrant copy number profiles, with frequent CNAs of genes in the p53 and RB pathways, whereas CNAs were less common in tumors with wild-type KIT. We evaluated the presence of four CNAs associated with high-risk tumor phenotypes as a means to predict aggressive tumors. Presence of these CNAs was able to predict high-risk phenotypes with a sensitivity of 75-91% and specificity of 86-93%, when using oaCGH and digital droplet PCR platforms. Further investigation of genome regions identified in this study may lead to the development of a molecular tool for classification and prognosis, as well as identification of therapeutic target molecules.

In this disclosure genome-wide DNA copy number profiling of a cohort of 109 primary MCTs, using oligo array comparative genomic hybridization (oaCGH) was performed. We identified a stepwise accumulation of CNAs in canine MCTs as tumor histological grade increases. We also demonstrated a strong correlation of several CNAs with the presence of KIT gene mutations, which is found in 20-30% of canine MCTs. Using four CNAs associated with high histological grade or KIT gene mutations, we developed and evaluated two simple digital droplet PCR (ddPCR) assays as a means to predict tumors with poor prognostic factors in 147 canine MCT specimens.

The relevant regions of CFA 5, CFA 20, & CFA 31 and more specifically the CFA 5:37 Mb, CFA 20:31 Mb, CFA 20:46 Mb and CFA 31:16 Mb regions may be found in Tables 3, 4, 5 and the Figures.

Method of Detection:

The copy number status of the regions assessed may be measured by, but is not limited to, fluorescence in situ hybridization (FISH), polymerase chain reaction (PCR), comparative genomic hybridization (CGH), or next generation sequencing (NGS). The biological sample must be a biopsy of the mass and may be a fresh sample, a fresh-frozen sample of the suspected mass, a sample in a preservative such as, for example, RNAlater, or a sample that has been processed for pathologic assessment. For example, the tissue specimen may have been soaked in one of several options to fix the tissues for histologic evaluation, such as, but not limited to, conventional histologic fixatives including, 10% neutral buffered formalin, B5, zinc-formalin. The sample may also have been soaked in formalin free fixatives such as, but not limited to, for example, 70% ethanol FineFIX, RCL-2 and HOPE.

The invention also provides a method of selecting treatment for a dog with MCT. The detection and quantification of the copy number status at regions of CFA 5, CFA 20 and CFA 31 would indicate the presence of a high-risk MCT and thus may be used to direct therapy accordingly. If the dog has MCT, the therapy may be, for example, surgical resection of the mass with wide margins, the extent of which is determined by the size and precise location of the mass, surgical resection followed by radiation therapy and/or chemotherapy such as, but not limited to, Vinblastine or Lumustine, and/or treatment with one or more tyrosine kinase inhibitors (TKIs) such as, toceranib (Palladia®) or masatinib (Kinavet®), and imatinib (Gleevac®).

5.1. Definitions

While the following terms are believed to be well understood by one of ordinary skill in the art, the following definitions are set forth to facilitate explanation of the presently disclosed subject matter.

Mast cells are derived from the bone marrow and can be found in various tissues throughout the body, generally residing in the connective tissues associated with the skin, lungs, nose, and mouth. The primary functions of mast cells are to aid tissue repair and the formation of new blood vessels and defend the body against parasitic infestations. In addition, mast cells contain several types of dark granules containing histamine and heparin, which are used by the body to modify immune reactions and inflammation. A “mast cell tumor” (MCT) is type of round-cell tumor containing mast cells, which may also be referred to as a mastocytoma. MCTs are found in humans and many animal species; in human medicine it also can refer to an accumulation or nodule of mast cells that resembles a tumor. MCTs are a common form of skin tumor in the domestic dog, accounting for up to 20% of all skin cancers in this species. The clinical behavior of MCTs varies widely from benign tumors, which can be cured by surgical removal alone, to highly malignant tumors that exhibit aggressive biologic behavior and high rates of metastasis, even in dogs treated with a combination of surgery, radiation therapy and chemotherapy.

“Copy number” is a measurement of DNA, whether of a single locus, one or more loci, or an entire genome. A “copy number” of two is “wild-type” in a dog (because of diploidy, except for sex chromosomes). A “copy number” of other than two in a dog (except for sex chromosomes) deviates from wild-type. Such deviations include gains, i.e., increases in copy number generally up to 5 copies per cell, deletions, i.e., decreases in copy number, i.e., either 1 or 0 copies per cell, and amplifications, i.e., increases in copy number generally in excess of 5 copies per cell.

“Labeled,” “labeled with a detectable label,” and “detectably labeled” are used interchangeably herein to indicate that an entity (e.g., a probe) can be detected. “Label” and “detectable label” mean a moiety attached to an entity to render the entity detectable, such as a moiety attached to a probe to render the probe detectable upon binding to a target sequence. The moiety, itself, may not be detectable but may become detectable upon reaction with yet another moiety. Use of the term “detectably labeled” is intended to encompass such labeling.

The detectable label can be selected such that the label generates a signal, which can be measured and the intensity of which is proportional to the amount of bound entity. A wide variety of systems for labeling and/or detecting molecules, such as nucleic acids, e.g., probes, are well-known. Labeled nucleic acids can be prepared by incorporating or conjugating a label that is directly or indirectly detectable by spectroscopic, photochemical, biochemical, immunochemical, electrical, optical, chemical or other means. Suitable detectable labels include radioisotopes, fluorophores, chromophores, chemiluminescent agents, microparticles, enzymes, magnetic particles, electron dense particles, mass labels, spin labels, haptens, and the like. Fluorophores and chemiluminescent agents are preferred herein.

“Nucleic acid sample” refers to a sample comprising nucleic acid in a form suitable for hybridization with a probe, such as a sample comprising nuclei or nucleic acids isolated or purified from such nuclei. The nucleic acid sample may comprise total or partial (e.g., particular chromosome(s)) genomic DNA, total or partial mRNA (e.g., particular chromosome(s) or gene(s)), or selected sequence(s). Condensed chromosomes (such as are present in interphase or metaphase) are suitable for use as targets in in situ hybridization, such as FISH.

“Predetermined cutoff” and “predetermined level” refer generally to a cutoff value that is used to assess diagnostic/prognostic/therapeutic efficacy results by comparing the assay results against the predetermined cutoff/level, where the predetermined cutoff/level already has been linked or associated with various clinical parameters (e.g., severity of disease, progression/nonprogression/improvement, etc.).

“Probe,” in the context of the present disclosure, is an oligonucleotide or polynucleotide that can selectively hybridize to at least a portion of a target sequence under conditions that allow for or promote selective hybridization. In general, a probe can be complementary to the coding or sense (+) strand of DNA or complementary to the non-coding or anti-sense (−) strand of DNA (sometimes referred to as “reverse-complementary”). Probes can vary significantly in length. A length of about 10 to about 100 nucleotides, such as about 15 to about 75 nucleotides, e.g., about 15 to about 50 nucleotides, can be preferred in some applications such as PCR, whereas a length of about 50 to about 1×10⁶ nucleotides can be preferred for chromosomal probes and a length of about 5,000 to about 800,000 nucleotides or more preferably about 75,000 to about 200,000 for BAC probes.

The invention encompasses fragments of nucleic acids that can serve (1) as probes for detecting segments of domestic dog (Canis familairis, CFA) genome referred to as chromosomes 5, 20 or 31 (hereafter referred to as CFA 5, CFA 20 and CFA 31). The dog genome has been sequenced and is available for example, USCS canfam2 at http://genome.ucsc.edu/cgi-bin/hgGateway?db=canFam2 and the NCBI Canis lupus familiaris genome database; or ENSEMBL database CanFam3.1 (GCA_000002285.2). See also, Lindblad-Toh et al. 2005 “Genome sequence, comparative analysis and haplotype structure of the domestic dog” Nature 438 (7069), 803-819.

The changes in copy number of regions of CFA 5, CFA 20 and/or CFA 31 may be detected by a number of methods well known in the art, e.g., Southern and northern blotting, dot blotting, colony hybridizations, hybridization to an array, comparative genomic hybridization (CGH), etc. or (2) as polymerase chain reaction (PCR) primers to amplify CFA 5, CFA 20 and/or CFA 31. PCR primers can comprise, in addition to CFA 5, CFA 20 and/or CFA 31 nucleic acid sequences, other sequences such as restriction enzyme cleavage sites that facilitate the use of the amplified nucleic acid. PCR is described in the following references: Saiki et al. 1988 Science 239 487-491; PCR Technology, Erlich, ed., Stockton Press, (1989). As explained below, PCR can be useful to detect abnormally low or high levels of target regions of chromosomes including CFA 5, CFA 20 and/or CFA 31.

Hybridization techniques are well known in the art and are described by Sambrook, J., E. F. Fritsch, and T. Maniatis (Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y., chapters 9 and 11, (1989)) and Current Protocols in Molecular Biology (F. M. Ausubel et al., eds., John Wiley & Sons, Inc., sections 2.10 and 6.3-6.4 (1995)), the relevant portions of which are incorporated by reference herein. Moderately stringent conditions for filter hybridizations include hybridization in about 50% formamide, 6×SSC at a temperature from about 42 C to 55 C and washing at about 60 C in 0.5×SSC, 0.1% SDS. Highly stringent conditions are defined as hybridization conditions as above, but with washing at approximately 68 C in 0.2×SSC, 0.1% SDS. SSPE (1×SSPE is 0.15 M NaCl, 10 mM NaH₂PO₄, and 1.26 mM EDTA, pH 7.4) can be substituted for SSC (1×SSC is 0.15 M NaCl and 1 5 mM sodium citrate) in the hybridization and wash buffers; washes, optionally at least two washes, are performed for 15 minutes after hybridization is complete.

It should be understood that the wash temperature and wash salt concentration can be adjusted as necessary to achieve a desired degree of stringency by applying the basic principles that govern hybridization reactions and duplex stability, as known to those skilled in the art and described further below (see e.g., Sambrook et al., supra). When nucleic acids of known sequence are hybridized, the hybrid length can be determined by aligning the sequences of the nucleic acids (for example, using GAP) and identifying the region or regions of optimal sequence complementarity. The hybridization temperature for hybrids anticipated to be less than 50 base pairs in length should be 5 to 10° C. less than the melting temperature (Tm) of the hybrid, where Tm is determined according to the following equations. For hybrids less than 18 base pairs in length, Tm (degrees C.)=2(# of A+T bases)+4(# of G+C bases). For hybrids above 18 base pairs in length, Tm (degrees C.)=81.5+16.6(log₁₀[Na+])+0.41 (% G+C)−(600 N), where N is the number of bases in the hybrid, and [Na+] is the concentration of sodium ions in the hybridization buffer. Each such hybridizing nucleic acid has a length that is at least 15 nucleotides (or at least 18 nucleotides, or at least 20, or at least 25, or at least 30, or at least 40, or at least 50, or at least 100. Sambrook et al., supra.

Throughout the present specification, the terms “about” and/or “approximately” may be used in conjunction with numerical values and/or ranges. The term “about” is understood to mean those values near to a recited value. For example, “about 40 [units]” may mean within ±25% of 40 (e.g., from 30 to 50), within ±20%, ±15%, ±10%, ±9%, ±8%, ±7%, ±6%, ±5%, ±4%, ±3%, ±2%, ±1%, less than ±1%, or any other value or range of values therein or therebelow. Furthermore, the phrases “less than about [a value]” or “greater than about [a value]” should be understood in view of the definition of the term “about” provided herein. The terms “about” and “approximately” may be used interchangeably.

Throughout the present specification, numerical ranges are provided for certain quantities. It is to be understood that these ranges comprise all subranges therein. Thus, the range “from 50 to 80” includes all possible ranges therein (e.g., 51-79, 52-78, 53-77, 54-76, 55-75, 60-70, etc.). Furthermore, all values within a given range may be an endpoint for the range encompassed thereby (e.g., the range 50-80 includes the ranges with endpoints such as 55-80, 50-75, etc.).

As used herein, the verb “comprise” as is used in this description and in the claims and its conjugations are used in its non-limiting sense to mean that items following the word are included, but items not specifically mentioned are not excluded.

Throughout the specification the word “comprising,” or variations such as “comprises” or “comprising,” will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps. The present disclosure may suitably “comprise”, “consist of”, or “consist essentially of”, the steps, elements, and/or reagents described in the claims.

It is further noted that the claims may be drafted to exclude any optional element. As such, this statement is intended to serve as antecedent basis for use of such exclusive terminology as “solely”, “only” and the like in connection with the recitation of claim elements, or the use of a “negative” limitation.

Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Preferred methods, devices, and materials are described, although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present disclosure. All references cited herein are incorporated by reference in their entirety.

Polynucleotide Amplification and Determination

In many instances, it is desirable to amplify a nucleic acid sequence using any of several nucleic acid amplification procedures which are well known in the art. Specifically, nucleic acid amplification is the chemical or enzymatic synthesis of nucleic acid copies which contain a sequence that is complementary to a nucleic acid sequence being amplified (template). The methods and kits of the invention may use any nucleic acid amplification or detection methods known to one skilled in the art, such as those described in U.S. Pat. No. 5,525,462 (Takarada et al.); U.S. Pat. No. 6,114,117 (Hepp et al.); U.S. Pat. No. 6,127,120 (Graham et al.); U.S. Pat. No. 6,344,317 (Urnovitz); U.S. Pat. No. 6,448,001 (Oku); U.S. Pat. No. 6,528,632 (Catanzariti et al.); and PCT Pub. No. WO 2005/111209 (Nakajima et al.); all of which are incorporated herein by reference in their entirety.

Commonly used methods known in the art for the quantification of mRNA expression in a sample include northern blotting and in situ hybridization (Parker and Barnes, Methods Mol. Biol. 106:247-83, 1999), RNAse protection assays (Hod, Biotechniques 13:852-54, 1992), PCR-based methods, such as reverse transcription PCR (RT-PCR) (Weis et al., TIG 8:263-64, 1992), and array-based methods (Schena et al., Science 270:467-70, 1995). Alternatively, antibodies may be employed that can recognize specific duplexes, including DNA duplexes, RNA duplexes, and DNA-RNA hybrid duplexes, or DNA-protein duplexes. Representative methods for sequencing-based gene expression analysis include Serial Analysis of Gene Expression (SAGE), bead-based technologies, single molecule fluorescence in situ hybridization (smFISH) studies, and gene expression analysis by massively parallel signature sequencing. Velculescu et al. 1995 Science 270 484-487; Streefkerk et al., 1976, Pro Biol Fluid Proc Coll 24 811-814; Soini U.S. Pat. No. 5,028,545; smFISH, Lyubimova et al. 2013 Nat Protocol 8(9) 1743-1758.

In some embodiments, the nucleic acids are amplified by PCR amplification using methodologies known to one skilled in the art. One skilled in the art will recognize, however, that amplification can be accomplished by any known method, such as ligase chain reaction (LCR), Qβ-replicase amplification, rolling circle amplification, transcription amplification, self-sustained sequence replication, nucleic acid sequence-based amplification (NASBA), each of which provides sufficient amplification. Branched-DNA technology may also be used to qualitatively demonstrate the presence of a sequence of the technology, which represents a particular methylation pattern, or to quantitatively determine the amount of this particular genomic sequence in a sample. Nolte reviews branched-DNA signal amplification for direct quantitation of nucleic acid sequences in clinical samples (Nolte, 1998, Adv. Clin. Chem. 33:201-235).

The PCR process is well known in the art and is thus not described in detail herein. For a review of PCR methods and protocols, see, e.g., Innis et al., eds., PCR Protocols, A Guide to Methods and Application, Academic Press, Inc., San Diego, Calif. 1990; U.S. Pat. No. 4,683,202 (Mullis); which are incorporated herein by reference in their entirety. PCR reagents and protocols are also available from commercial vendors, such as Roche Molecular Systems (Pleasanton, Calif.). PCR may be carried out as an automated process with a thermostable enzyme. In this process, the temperature of the reaction mixture is cycled through a denaturing region, a primer annealing region, and an extension reaction region automatically. Machines specifically adapted for this purpose are commercially available.

High Throughput, Single Molecule Sequencing, and Direct Detection Technologies

Suitable next generation sequencing technologies are widely available. Examples include the 454 Life Sciences platform (Roche, Branford, Conn.) (Margulies et al. 2005 Nature, 437, 376-380); Illumina's Genome Analyzer, GoldenGate Methylation Assay, or Infinium Methylation Assays, i.e., Infinium HumanMethylation 27K BeadArray or VeraCode GoldenGate methylation array (Illumina, San Diego, Calif.; Bibkova et al., 2006, Genome Res. 16, 383-393; U.S. Pat. Nos. 6,306,597 and 7,598,035 (Macevicz); U.S. Pat. No. 7,232,656 (Balasubramanian et al.)); or DNA Sequencing by Ligation, SOLiD System (Applied Biosystems/Life Technologies; U.S. Pat. Nos. 6,797,470, 7,083,917, 7,166,434, 7,320,865, 7,332,285, 7,364,858, and 7,429,453 (Barany et al.); or the Helicos True Single Molecule DNA sequencing technology (Harris et al., 2008 Science, 320, 106-109; U.S. Pat. Nos. 7,037,687 and 7,645,596 (Williams et al.); U.S. Pat. No. 7,169,560 (Lapidus et al.); U.S. Pat. No. 7,769,400 (Harris)), the single molecule, real-time (SMRT™) technology of Pacific Biosciences, and sequencing (Soni and Meller, 2007, Clin. Chem. 53, 1996-2001) which are incorporated herein by reference in their entirety. These systems allow the sequencing of many nucleic acid molecules isolated from a specimen at high orders of multiplexing in a parallel fashion (Dear, 2003, Brief Funct. Genomic Proteomic, 1(4), 397-416 and McCaughan and Dear, 2010, J. Pathol., 220, 297-306). Each of these platforms allow sequencing of clonally expanded or non-amplified single molecules of nucleic acid fragments. Certain platforms involve, for example, (i) sequencing by ligation of dye-modified probes (including cyclic ligation and cleavage), (ii) pyrosequencing, and (iii) single-molecule sequencing.

Pyrosequencing is a nucleic acid sequencing method based on sequencing by synthesis, which relies on detection of a pyrophosphate released on nucleotide incorporation. Generally, sequencing by synthesis involves synthesizing, one nucleotide at a time, a DNA strand complimentary to the strand whose sequence is being sought. Study nucleic acids may be immobilized to a solid support, hybridized with a sequencing primer, incubated with DNA polymerase, ATP sulfurylase, luciferase, apyrase, adenosine 5′ phosphsulfate and luciferin. Nucleotide solutions are sequentially added and removed. Correct incorporation of a nucleotide releases a pyrophosphate, which interacts with ATP sulfurylase and produces ATP in the presence of adenosine 5′ phosphsulfate, fueling the luciferin reaction, which produces a chemiluminescent signal allowing sequence determination. Machines for pyrosequencing and methylation specific reagents are available from Qiagen, Inc. (Valencia, Calif.). See also Tost and Gut, 2007, Nat. Prot. 2 2265-2275. An example of a system that can be used by a person of ordinary skill based on pyrosequencing generally involves the following steps: ligating an adaptor nucleic acid to a study nucleic acid and hybridizing the study nucleic acid to a bead; amplifying a nucleotide sequence in the study nucleic acid in an emulsion; sorting beads using a picoliter multiwell solid support; and sequencing amplified nucleotide sequences by pyrosequencing methodology (e.g., Nakano et al., 2003, J. Biotech. 102, 117-124). Such a system can be used to exponentially amplify amplification products generated by a process described herein, e.g., by ligating a heterologous nucleic acid to the first amplification product generated by a process described herein.

Certain single-molecule sequencing embodiments are based on the principal of sequencing by synthesis, and utilize single-pair Fluorescence Resonance Energy Transfer (single pair FRET) as a mechanism by which photons are emitted as a result of successful nucleotide incorporation. The emitted photons often are detected using intensified or high sensitivity cooled charge-couple-devices in conjunction with total internal reflection microscopy (TIRM). Photons are only emitted when the introduced reaction solution contains the correct nucleotide for incorporation into the growing nucleic acid chain that is synthesized as a result of the sequencing process. In FRET based single-molecule sequencing or detection, energy is transferred between two fluorescent dyes, sometimes polymethine cyanine dyes Cy3 and Cy5, through long-range dipole interactions. The donor is excited at its specific excitation wavelength and the excited state energy is transferred, non-radiatively to the acceptor dye, which in turn becomes excited. The acceptor dye eventually returns to the ground state by radiative emission of a photon. The two dyes used in the energy transfer process represent the “single pair”, in single pair FRET. Cy3 often is used as the donor fluorophore and often is incorporated as the first labeled nucleotide. Cy5 often is used as the acceptor fluorophore and is used as the nucleotide label for successive nucleotide additions after incorporation of a first Cy3 labeled nucleotide. The fluorophores generally are within 10 nanometers of each other for energy transfer to occur successfully. Bailey et al. recently reported a highly sensitive (15 pg methylated DNA) method using quantum dots to detect methylation status using fluorescence resonance energy transfer (MS-qFRET) (Bailey et al. 2009, Genome Res. 19(8), 1455-1461, which is incorporated herein by reference in its entirety).

An example of a system that can be used based on single-molecule sequencing generally involves hybridizing a primer to a study nucleic acid to generate a complex; associating the complex with a solid phase; iteratively extending the primer by a nucleotide tagged with a fluorescent molecule; and capturing an image of fluorescence resonance energy transfer signals after each iteration (e.g., Braslavsky et al., PNAS 100(7): 3960-3964 (2003); U.S. Pat. No. 7,297,518 (Quake et al.) which are incorporated herein by reference in their entirety). Such a system can be used to directly sequence amplification products generated by processes described herein. In some embodiments the released linear amplification product can be hybridized to a primer that contains sequences complementary to immobilized capture sequences present on a solid support, a bead or glass slide for example. Hybridization of the primer-released linear amplification product complexes with the immobilized capture sequences, immobilizes released linear amplification products to solid supports for single pair FRET based sequencing by synthesis. The primer often is fluorescent, so that an initial reference image of the surface of the slide with immobilized nucleic acids can be generated. The initial reference image is useful for determining locations at which true nucleotide incorporation is occurring. Fluorescence signals detected in array locations not initially identified in the “primer only” reference image are discarded as non-specific fluorescence. Following immobilization of the primer-released linear amplification product complexes, the bound nucleic acids often are sequenced in parallel by the iterative steps of, a) polymerase extension in the presence of one fluorescently labeled nucleotide, b) detection of fluorescence using appropriate microscopy, TIRM for example, c) removal of fluorescent nucleotide, and d) return to step a with a different fluorescently labeled nucleotide.

The technology may be practiced with digital PCR. Digital PCR was developed by Kalinina and colleagues (Kalinina et al., 1997, Nucleic Acids Res. 25; 1999-2004) and further developed by Vogelstein and Kinzler (1999, Proc. Natl. Acad. Sci. U.S.A. 96; 9236-9241). The application of digital PCR is described by Cantor et al. (PCT Pub. Nos. WO 2005/023091A2 (Cantor et al.); WO 2007/092473 A2, (Quake et al.)), which are hereby incorporated by reference in their entirety. Digital PCR takes advantage of nucleic acid (DNA, cDNA or RNA) amplification on a single molecule level, and offers a highly sensitive method for quantifying low copy number nucleic acid. Fluidigm® Corporation, BioRad's Digital PCR and Raindance technologies all offer systems for the digital analysis of nucleic acids. See, Karlin-Neumann G et al. (2012). Probing copy number variations using Bio-Rad's QX100™ Droplet Digital™ PCR system. Bio-Rad Bulletin 6277; Diderot et al., Clinical Chemistry February 2013 clinchem.2012.193409.

In some embodiments, nucleotide sequencing may be by solid phase single nucleotide sequencing methods and processes. Solid phase single nucleotide sequencing methods involve contacting sample nucleic acid and solid support under conditions in which a single molecule of sample nucleic acid hybridizes to a single molecule of a solid support. Such conditions can include providing the solid support molecules and a single molecule of sample nucleic acid in a “microreactor.” Such conditions also can include providing a mixture in which the sample nucleic acid molecule can hybridize to solid phase nucleic acid on the solid support. Single nucleotide sequencing methods useful in the embodiments described herein are described in PCT Pub. No. WO 2009/091934 (Cantor).

In certain embodiments, nanopore sequencing detection methods include (a) contacting a nucleic acid for sequencing (“base nucleic acid,” e.g., linked probe molecule) with sequence-specific detectors, under conditions in which the detectors specifically hybridize to substantially complementary subsequences of the base nucleic acid; (b) detecting signals from the detectors and (c) determining the sequence of the base nucleic acid according to the signals detected. In certain embodiments, the detectors hybridized to the base nucleic acid are disassociated from the base nucleic acid (e.g., sequentially dissociated) when the detectors interfere with a nanopore structure as the base nucleic acid passes through a pore, and the detectors disassociated from the base sequence are detected.

A detector also may include one or more regions of nucleotides that do not hybridize to the base nucleic acid. In some embodiments, a detector is a molecular beacon. A detector often comprises one or more detectable labels independently selected from those described herein. Each detectable label can be detected by any convenient detection process capable of detecting a signal generated by each label (e.g., magnetic, electric, chemical, optical and the like). For example, a CD camera can be used to detect signals from one or more distinguishable quantum dots linked to a detector.

Next generation sequencing techniques may be applied to measure expression levels or count numbers of transcripts using RNA-seq or whole transcriptome shotgun sequencing. See, e.g., Mortazavi et al. 2008 Nat Meth 5(7) 621-627 or Wang et al. 2009 Nat Rev Genet 10(1) 57-63.

Nucleic acids in the invention may be counted using methods known in the art. In one embodiment, NanoString's n Counter system may be used. Geiss et al. 2008 Nat Biotech 26(3) 317-325; U.S. Pat. No. 7,473,767 (Dimitrov). Alternatively, Fluidigm's Dynamic Array system may be used. Byrne et al. 2009 PLoS ONE 4 e7118; Helzer et al. 2009 Can Res 69 7860-7866. For reviews, see also Zhao et al. 2011 Sci China Chem 54(8) 1185-1201 and Ozsolak and Milos 2011 Nat Rev Genet 12 87-98.

The invention encompasses any method known in the art for enhancing the sensitivity of the detectable signal in such assays, including, but not limited to, the use of cyclic probe technology (Bakkaoui et al., 1996, BioTechniques 20: 240-8, which is incorporated herein by reference in its entirety); and the use of branched probes (Urdea et al., 1993, Clin. Chem. 39, 725-6; which is incorporated herein by reference in its entirety). The hybridization complexes are detected according to well-known techniques in the art.

Reverse transcribed or amplified nucleic acids may be modified nucleic acids. Modified nucleic acids can include nucleotide analogs, and in certain embodiments include a detectable label and/or a capture agent. Examples of detectable labels include, without limitation, fluorophores, radioisotopes, colorimetric agents, light emitting agents, chemiluminescent agents, light scattering agents, enzymes and the like. Examples of capture agents include, without limitation, an agent from a binding pair selected from antibody/antigen, antibody/antibody, antibody/antibody fragment, antibody/antibody receptor, antibody/protein A or protein G, hapten/anti-hapten, biotin/avidin, biotin/streptavidin, folic acid/folate binding protein, vitamin B12/intrinsic factor, chemical reactive group/complementary chemical reactive group (e.g., sulfhydryl/maleimide, sulfhydryl/haloacetyl derivative, amine/isotriocyanate, amine/succinimidyl ester, and amine/sulfonyl halides) pairs, and the like. Modified nucleic acids having a capture agent can be immobilized to a solid support in certain embodiments.

The invention described herein may be used in conjunction with other molecular techniques for detection of cancer such as US Pat Pub 2013/0171637 (Giafis et al.) the contents of which are hereby incorporated by reference in its entirety.

Statistical Methods

The data may be ranked for its ability to distinguish biomarkers in both the 1 versus all (i.e., disease versus normal) and the all-pairwise (i.e., normal versus specific disease) cases. One statistic used for the ranking is the area under the receiver operator characteristic (ROC) curve (a plot of sensitivity versus (1-specificity)). Although biomarkers are evaluated for reliability across datasets, the independent sample sets are not combined for the purposes of the ROC ranking. As a result, multiple independent analyses are performed and multiple independent rankings are obtained for each biomarker's ability to distinguish groups of interest.

It is to be understood that other genes and/or diagnostic criteria may be used in this invention. For example, animal characteristics, standard blood workups, the results of imaging tests, and/or histological evaluation may optionally be combined with biomarkers disclosed herein.

Such analysis methods may be used to form a predictive model, and then use that model to classify test data. For example, one convenient and particularly effective method of classification employs multivariate statistical analysis modeling, first to form a model (a “predictive mathematical model”) using data (“modeling data”) from samples of known class (e.g., from subjects known to have, or not have, a particular class, subclass or grade of lung cancer), and second to classify an unknown sample (e.g., “test data”), according to lung cancer status.

Pattern recognition (PR) methods have been used widely to characterize many different types of problems ranging for example over linguistics, fingerprinting, chemistry and psychology. In the context of the methods described herein, pattern recognition is the use of multivariate statistics, both parametric and non-parametric, to analyze spectroscopic data, and hence to classify samples and to predict the value of some dependent variable based on a range of observed measurements. There are two main approaches. One set of methods is termed “unsupervised” and these simply reduce data complexity in a rational way and also produce display plots that can be interpreted by the human eye. The other approach is termed “supervised” whereby a training set of samples with known class or outcome is used to produce a mathematical model and is then evaluated with independent validation data sets.

Unsupervised PR methods are used to analyze data without reference to any other independent knowledge. Examples of unsupervised pattern recognition methods include principal component analysis (PCA), hierarchical cluster analysis (HCA), and non-linear mapping (NLM).

Alternatively, and in order to develop automatic classification methods, it has proved efficient to use a “supervised” approach to data analysis. Here, a “training set” of biomarker expression data is used to construct a statistical model that predicts correctly the “class” of each sample. This training set is then tested with independent data (referred to as a test or validation set) to determine the robustness of the computer-based model. These models are sometimes termed “expert systems,” but may be based on a range of different mathematical procedures. Supervised methods can use a data set with reduced dimensionality (for example, the first few principal components), but typically use unreduced data, with all dimensionality. In all cases the methods allow the quantitative description of the multivariate boundaries that characterize and separate each class, for example, each class of lung cancer in terms of its biomarker expression profile. It is also possible to obtain confidence limits on any predictions, for example, a level of probability to be placed on the goodness of fit (see, for example, Sharaf; Illman; Kowalski, eds. (1986). Chemometrics. New York: Wiley). The robustness of the predictive models can also be checked using cross-validation, by leaving out selected samples from the analysis.

Examples of supervised pattern recognition methods include the following nearest centroid methods (Dabney 2005 Bioinformatics 21(22):4148-4154 and Tibshirani et al. 2002 Proc. Natl. Acad. Sci. USA 99(10):6576-6572); soft independent modeling of class analysis (SIMCA) (see, for example, Wold, (1977) Chemometrics: theory and application 52: 243-282.); partial least squares analysis (PLS) (see, for example, Wold (1966) Multivariate analysis 1: 391-420; Joreskog (1982) Causality, structure, prediction 1: 263-270); linear discriminant analysis (LDA) (see, for example, Nillson (1965). Learning machines. New York.); K-nearest neighbor analysis (KNN) (see, for example, Brown and Martin 1996 J Chem Info Computer Sci 36(3):572-584); artificial neural networks (ANN) (see, for example, Wasserman (1993). Advanced methods in neural computing. John Wiley & Sons, Inc; O'Hare & Jennings (Eds.). (1996). Foundations of distributed artificial intelligence (Vol. 9). Wiley); probabilistic neural networks (PNNs) (see, for example, Bishop & Nasrabadi (2006). Pattern recognition and machine learning (Vol. 1, p. 740). New York: Springer; Specht, (1990). Probabilistic neural networks. Neural networks, 3(1), 109-118); rule induction (RI) (see, for example, Quinlan (1986) Machine learning, 1(1), 81-106); and, Bayesian methods (see, for example, Bretthorst (1990). An introduction to parameter estimation using Bayesian probability theory. In Maximum entropy and Bayesian methods (pp. 53-79). Springer Netherlands; Bretthorst, G. L. (1988). Bayesian spectrum analysis and parameter estimation (Vol. 48). New York: Springer-Verlag); unsupervised hierarchical clustering (see for example Herrero 2001 Bioinformatics 17(2) 126-136). In one embodiment, the classifier is the centroid based method described in Mullins et al. 2007 Clin Chem 53(7):1273-9, which is herein incorporated by reference in its entirety for its teachings regarding disease classification.

It is often useful to pre-process data, for example, by addressing missing data, translation, scaling, weighting, etc. Multivariate projection methods, such as principal component analysis (PCA) and partial least squares analysis (PLS), are so-called scaling sensitive methods. By using prior knowledge and experience about the type of data studied, the quality of the data prior to multivariate modeling can be enhanced by scaling and/or weighting. Adequate scaling and/or weighting can reveal important and interesting variation hidden within the data, and therefore make subsequent multivariate modeling more efficient. Scaling and weighting may be used to place the data in the correct metric, based on knowledge and experience of the studied system, and therefore reveal patterns already inherently present in the data.

If possible, missing data, for example gaps in column values, should be avoided. However, if necessary, such missing data may be replaced or “filled” with, for example, the mean value of a column (“mean fill”); a random value (“random fill”); or a value based on a principal component analysis (“principal component fill”). Each of these different approaches will have a different effect on subsequent PR analysis.

“Translation” of the descriptor coordinate axes can be useful. Examples of such translation include normalization and mean centering. “Normalization” may be used to remove sample-to-sample variation. Many normalization approaches are possible, and they can often be applied at any of several points in the analysis. “Mean centering” may be used to simplify interpretation. Usually, for each descriptor, the average value of that descriptor for all samples is subtracted. In this way, the mean of a descriptor coincides with the origin, and all descriptors are “centered” at zero. In “unit variance scaling,” data can be scaled to equal variance. Usually, the value of each descriptor is scaled by 1/StDev, where StDev is the standard deviation for that descriptor for all samples. “Pareto scaling” is, in some sense, intermediate between mean centering and unit variance scaling. In pareto scaling, the value of each descriptor is scaled by 1/sqrt(StDev), where StDev is the standard deviation for that descriptor for all samples. In this way, each descriptor has a variance numerically equal to its initial standard deviation. The pareto scaling may be performed, for example, on raw data or mean centered data.

“Logarithmic scaling” may be used to assist interpretation when data have a positive skew and/or when data spans a large range, e.g., several orders of magnitude. Usually, for each descriptor, the value is replaced by the logarithm of that value. In “equal range scaling,” each descriptor is divided by the range of that descriptor for all samples. In this way, all descriptors have the same range, that is, 1. However, this method is sensitive to presence of outlier points. In “autoscaling,” each data vector is mean centered and unit variance scaled. This technique is a very useful because each descriptor is then weighted equally and large and small values are treated with equal emphasis. This can be important for analytes present at very low, but still detectable, levels.

Several supervised methods of scaling data are also known. Some of these can provide a measure of the ability of a parameter (e.g., a descriptor) to discriminate between classes, and can be used to improve classification by stretching a separation. For example, in “variance weighting,” the variance weight of a single parameter (e.g., a descriptor) is calculated as the ratio of the inter-class variances to the sum of the intra-class variances. A large value means that this variable is discriminating between the classes. For example, if the samples are known to fall into two classes (e.g., a training set), it is possible to examine the mean and variance of each descriptor. If a descriptor has very different mean values and a small variance, then it will be good at separating the classes. “Feature weighting” is a more general description of variance weighting, where not only the mean and standard deviation of each descriptor is calculated, but other well-known weighting factors, such as the Fisher weight, are used.

The methods described herein may be implemented and/or the results recorded using any device capable of implementing the methods and/or recording the results. Examples of devices that may be used include but are not limited to electronic computational devices, including computers of all types. When the methods described herein are implemented and/or recorded in a computer, the computer program that may be used to configure the computer to carry out the steps of the methods may be contained in any computer readable medium capable of containing the computer program. Examples of computer readable medium that may be used include but are not limited to diskettes, CD-ROMs, DVDs, ROM, RAM, and other memory and computer storage devices. The computer program that may be used to configure the computer to carry out the steps of the methods and/or record the results may also be provided over an electronic network, for example, over the internet, an intranet, or other network.

The process of comparing a measured value and a reference value can be carried out in any convenient manner appropriate to the type of measured value and reference value for the discriminative gene at issue. “Measuring” can be performed using quantitative or qualitative measurement techniques, and the mode of comparing a measured value and a reference value can vary depending on the measurement technology employed. For example, when a qualitative colorimetric assay is used to measure expression levels, the levels may be compared by visually comparing the intensity of the colored reaction product, or by comparing data from densitometric or spectrometric measurements of the colored reaction product (e.g., comparing numerical data or graphical data, such as bar charts, derived from the measuring device). However, it is expected that the measured values used in the methods of the invention will most commonly be quantitative values. In other examples, measured values are qualitative. As with qualitative measurements, the comparison can be made by inspecting the numerical data, or by inspecting representations of the data (e.g., inspecting graphical representations such as bar or line graphs).

The process of comparing may be manual (such as visual inspection by the practitioner of the method) or it may be automated. For example, an assay device (such as a luminometer for measuring chemiluminescent signals) may include circuitry and software enabling it to compare a measured value with a reference value for a biomarker protein. Alternately, a separate device (e.g., a digital computer) may be used to compare the measured value(s) and the reference value(s). Automated devices for comparison may include stored reference values for the biomarker protein(s) being measured, or they may compare the measured value(s) with reference values that are derived from contemporaneously measured reference samples (e.g., samples from control subjects).

As will be apparent to those of skill in the art, when replicate measurements are taken, the measured value that is compared with the reference value is a value that takes into account the replicate measurements. The replicate measurements may be taken into account by using either the mean or median of the measured values as the “measured value.”

The invention also includes methods of identifying animals for particular treatments or selecting animals for which a particular treatment would be desirable or contraindicated.

The methods above may be performed by a reference laboratory, a veterinary hospital pathology laboratory, a university veterinary laboratory, a veterinarian's office or a veterinarian. The methods above may further comprise an algorithm and/or statistical analysis.

Samples

The sample may be a biopsy specimen of the suspected mass. For detection of the copy number status by FISH, cells from the mass are used to provide templates for the FISH probes. For PCR and DNA sequence based assays, the required template DNA may be obtained from the cells of the suspected mass.

Compositions and Kits

The invention provides compositions and kits for detecting a mast cell tumor in a dog comprising: (a) at least one reagent selected from the group consisting of: a nucleic acid probe capable of specifically detecting target regions of CFA 5, CFA 20 or CFA 31; and (b) instructions for use in measuring a copy number of these region of CFA 5, CFA 20 or CFA 31 in a biological sample from a dog wherein if the copy number status of the regions of CFA 5, CFA 20 and/or CFA 31 differ from that of a normal control.

The instructions comprise determining in a sample of relevant cells obtained from the dog the presence of chromosomal abnormalities, wherein the presence of chromosomal abnormalities involving at least two of the probes indicates that the patient has mast cell tumor. Such kits may further comprise, or consist of, blocking agents or other probes, various labels or labeling agents to facilitate detection of the probes, reagents for hybridization (e.g., buffers), a metaphase spread, and the like.

The following Examples further illustrate the disclosure and are not intended to limit the scope. In particular, it is to be understood that this disclosure is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present disclosure will be limited only by the appended claims.

6. EXAMPLES

Materials and Methods

Tissue Specimens

The retrospective cohort study recruited 147 formalin fixed paraffin embedded (FFPE) tissue specimens of canine cutaneous MCTs that had been diagnosed between 2003 and 2012. Initial diagnosis for each case was based on histological evaluation of the FFPE specimen by a veterinary pathologist. To eliminate inter-observer variation in tumor grading, the H&E slides were re-evaluated for 3-tier and 2-tier tumor grading and mitotic index by a single board-certified veterinary pathologist. H&E-stained slides of each specimen were assessed used to identify and mark regions enriched for neoplastic regions, excluding any surrounding normal tissues. A series of 25-μm slices was then obtained from the corresponding FFPE tissue block, and regions of non-tumor tissues removed by macrodissection. Tumor DNA was isolated using a QIAamp FFPE DNA extraction kit (Qiagen, Valencia, Calif., USA). Spectrophotometry (NanoDrop, Thermo Scientific, Wilmington, Del.) and agarose gel electrophoresis were used to determine DNA quantity and integrity. Sample information is shown in Table 1.

KIT Gene Mutation Analysis

Activating KIT gene mutations are reported to be present in 10-30% of canine MCTs, with presence of a mutation being correlated with poor clinical outcomes (Gil da Costa, 2015). PCR amplification for exons 8, 9, and 11 of the canine KIT gene was performed using Taq RED Master Mix Kit (Genesee Scientific, San Diego, Calif., USA). Primers were designed using primer-BLAST software (www.ncbi.nlm.nih.gov/tools/primer-blast/) and are shown in Table 2. The presence of internal tandem repeat (ITD) mutation in exons 8 and 11 was visualized using agarose gel electrophoresis. When an ITD mutant band was present in the PCR products of exons 8 or 11, the band was excised and purified using QIAquick Gel Extraction Kit (QIAGEN), followed by DNA (Sanger) sequencing analysis. PCR products without evidence of ITD mutations were subjected to direct sequencing to detect other sequence changes of minor frequencies. All sequencing was performed at the North Carolina State University Genome Research Laboratory (research.ncsu.edu/gsl/). The sequencing data were analyzed using 4peaks software (nucleobyte.com) and were compared with the canine KIT gene reference sequence (GeneID: AY313776).

Array Comparative Genomic Hybridization

oaCGH analysis was performed on DNA samples from 109 primary tumors using Agilent SurePrint G3 Canine Genome 180K microarrays which contain 171,534 coding and noncoding 60-mer oligonucleotide sequences spaced at ˜13 kb intervals (AMADID 25522, Agilent Technologies, Santa Clara, Calif., USA). A sex-matched equimolar pool of genomic DNA from peripheral blood of >100 healthy dogs was used as a common reference. Probe preparation, array hybridization, and post-hybridization washing were performed as reported previously (Thomas et al., 2014, Poorman et al., 2015, Roode et al., 2015, Shapiro et al., 2015). Scanned data were extracted using Feature Extraction Software v10.10 (Agilent Technologies) and assessed for data quality using Agilent QC metrics. Extracted data were filtered to exclude probes displaying non-uniform hybridization or signal saturation and imported into Nexus Copy Number software v7.5 (Biodiscovery Inc., El Sequendo, Calif., USA). Data was normalized using the FASST2 segmentation algorithm. Genomic copy number aberrations were defined as a minimum of three consecutive probes with log 2 tumor: reference values >0.201 (gain) or <−0.234 (loss). CNAs were defined as recurrent when CNAs were present in >20% of cases. The megabase (Mb) location of dog genes along the corresponding chromosome were based on the CanFam v3 genome sequence assembly accessed via the UCSC genome browser (genome.ucsc.edu/).

The Genomic Identification of Significant Targets in Cancer (GISTIC) algorithm was used to identify regions across the genome in tumors with and without KIT mutations with a statistically high frequency of aberration over the background (Q-bound<0.05, G score≥−1.0), indicating that these regions are more likely to contain a functional mutation associated with driving cancer pathogenesis (Beroukhim et al., 2007). Chromosomal regions were identified have having significantly different aberration frequencies between two groups when they had ≥50% difference between two groups also had a P<0.05 and Q-bound<0.05 based on a two-tailed Fisher's exact test.

Digital PCR Analysis

Four ddPCR assays, each comprising two PCR primers and a TaqMan® probe, were designed within the genome sequences of high penetrance of copy number change at each of CFA 5:37 Mb, CFA 20:31 Mb, CFA 20:46 Mb and CFA 31:16 Mb. Primers and TaqMan® probes were designed using PrimerQuest (www.idtdna.com) and Prime-BLAST (www.ncbi.nlm.nih.gov/tools/primer-blast/) software. For each assay, a double-Quenched probe (Integrated DNA Technologies, Coralville, Iowa) was used to reduce background fluorescence (5′-FAM/ZEN/3′-IBFQ probe for CFA 20:46 Mb and CFA 31: 16 Mb assays and HEX/ZEN/3′-IBFQ probe for CFA 5: 37 Mb and CFA 20: 31 Mb assays). Sequences and locations of primers and probes used in this study are shown in Table 2.

Two duplex ddPCR reactions were performed using a combination of CFA 20:46 Mb and CFA 20: 31 Mb (CFA20^(46Mb/31Mb) assay), and CFA 31:16 Mb and CFA 5: 37 Mb (CFA 31/CFA 5 assay). Each reaction mixture comprised 1× Droplet Supermix (Bio-Rad), 500 nM of each primer, 250 nM of FAM- and HEX-labeled probes and ˜55 ng of genomic DNA. The PCR reaction mixtures were partitioned into an emulsion of ˜20,000 droplets (mean±SD: 17,572±2,153 droplets/reaction) using a QX200™ Droplet Generator (Bio-Rad Laboratories, Richmond, Calif.). PCR was performed on T100™ Thermal Cycler (Bio-Rad) using thermal cycle condition as follows: denaturation at 95° C. for 10 min; 40 cycles of 94° C. for 30 sec and 58° C. for 60 sec; 98° C. for 10 min. Post PCR, droplets were analyzed on QX200™ Droplet Reader (Bio-Rad). The ratios of CFA 20: 46 Mb and CFA 20:31 Mb (CFA 20^(46Mb/31Mb) ratio) and CFA 31:16 Mb and CFA 5:37 Mb (CFA 31/CFA 5 ratio) were calculated on the Poisson distribution using Quantasoft™ software V1.7.4 (Bio-Rad). Comparison of copy number ratios determined by ddPCR and aCGH was performed as previously described (Mochizuki et al., 2015). To consolidate these two ratios into one parameter, score of CNAs associated with high-risk MCTs (CNA^(MCT) score) was calculated and assessed to predict tumors with high-risk phenotypes using the following equation:

CNA^(MCT) score=CFA20^(46Mb/31Mb) ratio×CFA31/CFA5 ratio

Statistical Analysis

Association analysis was performed with Fisher's exact test or Pearson's chi-squared test to evaluate difference in frequencies between groups, and with Wilcoxon rank-sum test to compare continuous values between groups. The correlation of two values was evaluated with Pearson's correlation coefficient analysis. To determine the suitable threshold for discriminating high-risk MCTs from low-risk MCTs, a receiver operating characteristic (ROC) curve analysis was performed. Statistical analyses were performed using JMP software v11 (SAS Institute, Cary, N.C.). Significance was set at P<0.05.

Results

Pathological Findings

A total of 147 canine MCTs were included in the present study. Of these, genome-wide DNA copy number profiles were obtained for 109 tumors by oaCGH (oaCGH cohort). The remaining 38 tumors served as a validation cohort for ddPCR analysis. Tumors were graded by 3-tier histological grading (Grade 1: 18 tumors, Grade 2: 93 tumors and Grade 3: 36 tumors) and by 2-tier histological grading (low grade: 87 tumors and high grade: 60 tumors). There was no significant difference in histological grading between the oaCGH and validation cohorts.

Canine MCT Show Stepwise Accumulation of CNAs as Histological Grade Increases

The oaCGH analysis of DNA isolated from 109 canine MCT tumor specimens revealed that all MCT cases showed various numbers (16-575, median: 107) of CNAs throughout the genome. Whole chromosome and subchromosomal CNAs were detected in canine MCTs (CNA size: <26 kilobases (kb) to 117 megabases (Mb), median: 359 kb). Representative genome-wide DNA copy number profiles are shown in FIG. 1. Segregation of oaCGH data by 3-tier and 2-tier histological grading schemes revealed that higher-grade tumors had significantly more CNAs than low- or intermediate-grade counterparts in both numbers and altered length (FIG. 2). Penetrance plots demonstrated increased frequency of CNAs in a stepwise manner, as tumor histological grade increases (FIG. 3).

KIT Gene Mutation Status and its Association with CNAs in Canine MCTs

A KIT gene mutation was detected in 42 of 147 (29%) tumors, with increased frequency in high-grade tumors (in 3-tier grading system, 6%, 23% and 56% in grades 1, 2 and 3 tumors, respectively, P<0.0001; in 2-tier grading system, 13% and 52% in low-grade and high-grade tumors, respectively, P<0.0001). The six most common mutations detected were 1) an ITD mutation in exon 11, with or without involvement of intron 11 and exon 12 (hereafter called as exon 11 ITD mutations), accounting for 74% ( 31/42) of all KIT mutations, followed by 2) Exon 11 indel (9.5%), 3) Exon 8 ITD (4.8%), 4) Exon 9 S479I (4.8%), 5) Exon 9 N508I (4.8%), and 6) Exon 8 Q430R (2.4%) mutations. Frequencies of the KIT mutation were not different in the aCGH and validation cohorts.

Segregation of oaCGH data by KIT mutational status demonstrated that tumors with KIT mutations exhibited significantly more CNAs, both in number and total length, than wild type (FIG. 4A). Genomic imbalance in 76 tumors with wild-type KIT gene (wt-KIT tumors) was limited to primarily small aberrations of low frequencies (FIG. 4B). The only recurrent CNAs>1 Mb were deletion of a mid-segment of CFA 20 (at 15-43 Mb) and/or gain of the distal portion of CFA 20 (at 45-50 Mb), detected in ˜25% of wt-KIT tumors. Frequency of these CNAs significantly increased as histological grade increases, suggesting a possible association of this genomic aberration on CFA 20 with a malignant phenotype in wt-KIT tumors (FIG. 8).

Recurrent CNAs in KIT mutant tumors (mut-KIT tumors) include whole chromosome gain of CFA 4, 13, 31, 36 and 38 and loss of CFA 5, 16 and 28 as well as many subchromosomal changes (FIG. 4B). When mut-KIT tumors were further segregated by tumors with exon 11 ITD mutations (n=24) and those with other types of mutation (n=9), there was no significant difference in number, total length, and frequency of CNAs between two groups, resulting in similar penetrance plots (FIG. 9). Unlike wt-KIT tumors, further segregation of mut-KIT tumors by their histological grading did not show any CNAs that differed statistically in frequency between groups, partly due to their small sample sizes (FIG. 10). Therefore, mut-KIT tumors were treated as a single entity for further analysis, regardless of specific mutation type and histological grade.

Comparison analysis of CNAs detected in wt-KIT and mut-KIT tumors identified that deletion of CFA 5, including TP53 (CFA 5: 32.5 Mb, 2.6% vs 60.6%) and gain of CFA 31, including RUNX1 (CFA31: 30.3 Mb, 6.6% vs 63.6%), and a small number of subchromosomal regions on CFA36, occurred significantly more frequently in mut-KIT tumors, whereas there was no region occurring more frequently in wt-KIT tumors (Table 3).

These CNAs were further interrogated using GISTIC analysis to differentiate CNAs containing possible pathogenic genes from background random CNAs. GISTIC analysis identified 131 CNAs (72 gain and 59 loss) in wt-KIT tumors, including gain of regions containing TERT (CFA34: 11.3 Mb, G-score=10.20, Q-bound=3.28×10⁻¹⁰) and CDK4 (CFA10: 1.8 Mb, G-score=7.18, Q-bound=2.89×10⁻⁶). In mut-KIT tumors, GISTIC analysis identified 62 discrete CNAs (23 gain and 39 loss) including a copy number gain of CFA13: 47.0 Mb that flanks the KIT oncogene (CFA13: 47.1 Mb, G-score=7.64, Q-bound=2.01×10⁻⁵), as well as gains of other oncogenes such as RUNX1 (CFA31: 30.3 Mb, G-score=13.43, Q-bound=4.29×10⁻¹⁵) and MDM4 (CFA38: 1.0 Mb, G-score=6.60, Q-bound=4.64×10⁻⁴). Details of CNAs identified by GISTIC analysis are provided in Tables 4 and 5 for wt-KIT tumors and mut-KIT tumors, respectively.

CNAs Associated with High-Risk MCTs

From these data, we hypothesized that several CNAs may be used to segregate biologically aggressive MCTs from benign tumors. Due to the unavailability of clinical outcomes of the cohort, we used histological grading score and KIT mutational status to define “high-risk MCTs”. As the 2-tier histological grading has been demonstrated to be superior in predicting clinical outcomes with higher inter-observer consistency (Kiupel et al., 2011, Takeuchi et al., 2013, Sabattini et al., 2015), we only used the 2-tier grading system (low or high grade) hereafter. We defined tumors with high histological grade and/or KIT mutations as “high-risk MCT” and those with low histological grade without KIT mutations as “low-risk MCTs”, based on the 2-tier histological grade and KIT mutational status. As mut-KIT tumors and high-grade wt-KIT tumors showed dissimilar copy number profiles, we sought regions where frequencies of CNAs are significantly higher in (1) mut-KIT tumors and (2) high-grade wt-KIT tumors, compared to low-grade wt-KIT tumors, to find genomic aberrations to separate high-risk and low-risk MCTs.

Comparison analysis revealed that genomic loss of CFA 5 and genomic gain of CFA 31 with a few subchromosomal CNAs occurred at significantly higher frequencies in mut-KIT tumors, compared to low-grade wt-KIT tumors (FIG. 6A). The comparison of high-grade and low-grade wt-KIT tumors revealed that only loss of middle portion of CFA 20 and gain of distal portion of CFA 20 were significantly different between two groups (FIG. 6B). Based on these findings, we then chose loss of CFA 5: 37.8 Mb and gain of CFA 31: 16.7 Mb as markers to differentiate mut-KIT tumors from low-risk MCTs and loss of CFA 20: 31.6 Mb and gain of CFA20: 46.4 Mb as markers to separate high-grade wt-KIT tumors from low-risk MCTs. Each case was then evaluated to have these alterations to predict high-risk MCTs. The presence of at least one of these four CNAs was able to predict high-risk MCTs with sensitivity of 91% and specificity of 89% in the cohort.

Digital PCR Analysis

Based on the aCGH data, two duplex ddPCR assays were designed to detect four CNAs associated with high-risk tumor phenotypes by calculating CFA 20^(46Mb/31Mb) ratio and CFA 31/CFA 5 ratio. The performance of the ddPCR assays was evaluated by comparing copy number ratios derived from oaCGH and ddPCR analyses. The results of two platforms were well correlated in 109 tumors with oaCGH profiles (R²=0.93 for CFA 20^(46Mb/31Mb) ratio and 0.78 for CFA 31/CFA 5 ratio, FIG. 7). High-risk tumors showed significant increase in the CNA^(MCT) score as well as the CFA 20^(46Mb/31Mb) and CFA 31/CFA 5 ratios (all, P<0.0001, FIG. 7). ROC curve analysis was performed to evaluate CNA^(MCT) score as an indicator for high-risk MCTs. The area under the ROC curve was 0.89. A cut-off CNA^(MCT) score of 1.27 was determined at a point where the sum of sensitivity and specificity values was highest. At this cut-off point, the sensitivity and specificity of high-risk MCT detection in the oaCGH cohort were 76% ( 42/55) and 96% ( 52/54), respectively.

The CNA^(MCT) score was then evaluated in a validation cohort consisting of 38 MCTs. The CNA^(MCT) score showed significant increase in high-grade tumors (P=0.007, SUPPLEMENT FIG. 4) and was able to predict high-risk tumor phenotype with the sensitivity of 69% ( 11/16) and specificity of 86% ( 19/22) at the cut-off value of 1.27. Since there was no statistical difference in sensitivity and specificity between the oaCGH cohort and validation cohorts, these two cohorts were combined. Overall sensitivity and specificity of the CNA^(MCT) score in 147 MCTs for the prediction of high-risk tumors were 75% and 93%, respectively. Four of five low-risk MCTs misclassified by the CNA^(MCT) score were Patnaik's grade 2 tumors with mitotic index of 2-4, while remaining one was grade 1 tumor with mitotic index of 0. By evaluating a DNA samples from a canine MCT for the presence of a KIT exon 11 IDT and for the CNA^(MCT) score, the combined sensitivity and specificity to predict a high-risk tumor phenotype is 89% and 93%, respectively.

Discussion

In this study, genome-wide DNA copy number analysis of 109 canine MCTs revealed the heterogeneous nature of genomic alteration in canine MCTs. Comparison of copy number profiles of tumors of different histological grades demonstrated a stepwise accumulation of copy number alterations as histological grade increases, suggesting that these genomic alterations contribute to the aggressive biological behavior.

The KIT signaling pathway plays a critical role in the survival and proliferation of mast cells (Roskoski, 2005). Activating mutations of the KIT gene results in constitutive activation of KIT protein without ligand binding, resulting in neoplastic transformation of mast cells. Indeed, more than 80% of human mast cell neoplasms harbor the KIT gene mutations (Haenisch et al., 2012), whereas the mutation is less frequent (20-30%) in canine MCTs (Letard et al., 2008, Takeuchi et al., 2013). In this study, distinct copy number profiles between MCTs with and without KIT mutations were demonstrated with extensive CNAs affecting multiple chromosomes in mut-KIT tumors. GISTIC analysis identified a significant peak of gain at KIT locus on CFA 13 as a potential region associated with driving cancer pathogenesis in mut-KIT MCTs. Although whole chromosomal gain of CFA 13 is the most common recurrent chromosomal abnormality across canine cancers (Thomas et al., 2009, Angstadt et al., 2011, Hedan et al., 2011, Thomas et al., 2011, Thomas et al., 2014, Poorman et al., 2015, Roode et al., 2015, Shapiro et al., 2015), the GISTIC analysis successfully identified the KIT locus in mut-KIT tumors by taking into account both frequency and magnitude of copy number change, proving the utility of this analysis to identify pathogenic region and potential somatic mutations in cancer (Beroukhim et al., 2007).

Loss of CFA 5 occurred in >50% of mut-KIT MCTs, but is a genomic alteration uncommon in other canine cancers. Among many tumor suppressor genes located on CFA 5, loss of TP53 may represent a key genetic event in the development and progression of mut-KIT MCTs. In addition to loss of TP53 detected in 57.6% of mut-KIT tumors, GISTIC analysis identified a significant peak of gain on CFA 38 encompassing MDM4, which is a family member of MDM2 and serves as a negative regulator of tumor suppressor p53. Frequent gains of MDM4 (48.5%) and MDM2 (9.1%), coupled with frequent loss of TP53, highlights that disruption of the p53 pathway may be a key molecular alteration in mut-KIT MCTs as 75.8% of this subtype harbored one or more these CNAs, while these CNAs were present in only 6.6% of wt-KIT tumors.

Similar to these CNAs that could lead to the p53 pathway deregulation, CNAs involving genes of the RB pathway, another major tumor suppressor pathway regulating cell cycle and replication, were also common in mut-KIT tumors. These CNAs include loss of CDKN2A/p16 (CFA 11: 41.2 Mb, 21.2% of mut-KIT tumors), loss of RB1 (CFA 22: 3.1 Mb, 18.2%), gain of CDK4 (CFA 10: 1.8 Mb, 15.2%), and gain of CDK6 (CFA 14: 18.3 Mb, 24.2%), leading to deregulation of the RB pathway in 48.5% of mut-KIT MCTs, whereas only 23.7% of wt-KIT tumors presented with these CNAs. A previous study also identified altered expressions of genes involved in the cell cycle and the p53 pathway in undifferentiated MCTs (Giantin et al., 2014), suggesting that frequent CNAs involved in these two major tumor suppressor pathways may change expression levels of these genes and result in the aggressive biological behavior of mut-KIT MCTs.

Compared to mut-KIT tumors, copy number changes of wt-KIT tumors were very limited, with the only recurrent chromosomal alterations >1 Mb being a ˜30 Mb loss of CFA 20:15-43 Mb and a ˜5 Mb gain of CFA 20: 45-50 Mb. Gain of the distal portion of CFA 20 has been detected previously in canine osteosarcoma, hemangiosarcoma, benign melanocytoma and acute leukemias (Angstadt et al., 2011, Thomas et al., 2014, Poorman et al., 2015, Roode et al., 2015), loss of mid-region of CFA 20 is uncommon in canine cancers, suggesting that this genomic alteration may play an important role in the development of MCTs in a KIT-independent manner.

Interestingly, a recent genome-wide association studies in golden retrievers identified SNPs located at CFA 20: 31 Mb-50 Mb (CanFam v3) that are associated with increased risk of developing MCTs (Arendt et al., 2015). A SNP in the GNAI2 gene (CFA20: 39,080,161), which introduce an alternative splice of this gene resulting in a truncated protein, shows a strong association with development of MCTs, along with other SNPs in this region encompassing hyaluronidase genes. These SNPs should be further examined to determine any associated with the development of MCTs and also with progression to a malignant tumor phenotype in the general dog population.

Along with mid-region of CFA 20, GISTIC analysis also identified significance of copy number gains of TERT (CFA34: 11.3 Mb, 36.8% of wt-KIT tumors) and CDK4 (CFA10: 1.8 Mb, 23.7%) in wt-KIT tumors. It is of note that frequencies of these CNAs increased in tumors of higher grade suggesting their potential roles in the tumor progression. This is of particular interest for discovery of therapeutic molecular targets for wt-KIT tumors, since KIT-target therapy is less effective in dogs with MCTs without KIT gene mutations compared to mut-KIT tumors (London et al., 2009, Hahn et al., 2010). Further studies will elucidate the molecular abnormalities crucial for the progression from benign MCTs to malignant tumors in a KIT-independent manner.

A major challenge in the clinical management of canine MCTs lies in accurate prognostication. The application of molecular profiling analysis may provide an objective means to predict clinical outcomes of this cancer. Molecular profiling using gene expression analysis has been shown to separate differentiated and undifferentiated MCTs and predict clinical outcomes by quantifying expression of 13 genes (Giantin et al., 2014). In this study, we were able to predict high-risk MCTs with a sensitivity of 91% and a specificity of 89% using just four CNAs on a genome-wide oaCGH platform. To reduce the cost and processing time, we developed a simple ddPCR assay to detect these four CNAs. The ddPCR test was able to predict high-risk tumor phenotypes with sensitivity of 75% and specificity of 93%, indicating its potential use for risk stratification of canine MCTs. Whereas specificity was comparable between the two platforms, the oaCGH showed superior sensitivity in detecting these CNAs. This is partly because the specimen used in this study was degraded FFPE tissue-derived DNA, which results in generation of single-stranded DNA and artificial copy number alterations (Bhat et al., 2010, Bhat et al., 2011). Although two platforms showed a good correlation in copy number assessment, it is pertinent that DNA degradation may lead to false-negative results in the ddPCR. Another possible advantage of genome-wide oaCGH is the simultaneous detection of less frequent CNAs in addition to the four CNAs examined by the ddPCR, which may help refine the detection algorithm. Although the ddPCR is a fast, cost-effective way of detecting high-risk tumor-associated CNAs, refinement of the assay may be necessary to improve sensitivity.

One major limitation of this study was lack of the clinical outcome of the cases, limiting the analysis of clinical relevance of these molecular alterations. Future studies using clinical specimens (e.g., cytological slides) in a cohort of dogs with MCTs, where clinical outcomes, including treatment and survival time are available, are necessary to evaluate the value of these CNAs as a prognostic indicator in a clinical setting.

In Table 6, the copy number variations (CNV) described herein were used to differentiate high-risk MCTs (Mut-KIT tumors and high-grade tumors) and low-risk MCTs (wild-type KIT, low-grade tumors) with the sensitivity of 91% and specificity of 89%.

In summary, we characterized CNAs of canine MCTs and identified different genomic imbalances between tumors of different histological grades and those of different KIT mutation status. Additional investigation will aid further clarification regarding genes within copy number aberrant regions that are important in the development and progression of canine MCTs. Four CNAs identified in this study may serve as an objective, rapid molecular assay for the identification of aggressive MCTs.

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TABLE 1 Clinical and histopathologic findings in 109 canine mast cell tumors Breed Labrador Retriever (34), Boxer (32), Pug (32), Golden Retriever (8), Mixed (6), other breeds (35, each <5) Gender Female (77), Male (70) 3-tier histopathlogical grading Grade 1 (18), Grade 2 (93), Grade 3 (36) 2-tier histopathlogical grading Low (87), High (62)

TABLE 2 Primer and probe sequences used in this study Target Forward (SEQ ID NOS: 1-7) Reverse (SEQ ID NOS: 8-14) KIT exon 8 GGTGAGGTGTTCCAGCAGTC CCTTCCCTCGTGCACATTA KIT exon 9 ACGTATGCCAATTCCAATGT GCCATTGATGGAATGGACTT KIT exon 11 AAAGATGATCTGTCTCTCTTTTCTCC GAAGCTAATGGGGTTCCCTAA CFA 5: 37 Mb TTGCCTAGAGTATGGCGAAG GTCATGTGTTAGCAGAGAGAGA CFA 20: 31 Mb GGGTTACCCAAAGTAGTCTGA TACTTTGTCTTCCTGGTTGTAACT CFA 20: 46 Mb AAGGTTCTTAGCACAGAGGAATC AACATCCTATGCTCTGCTTCTG CFA 31: 16 Mb AGCTGGATTTACAAATAGCACTTTC GGAAGAGAAGGCAATGAATAGGA Target Probe (SEQ ID NOS: 15-18) Location CFA 5: 37 Mb TTGGCTGTTCTCTCTGTGTCCGTT CFA 5: 37,905,014-37,905,113 CFA 20: 31 Mb TTTGGCTACCCTTCCTAAGACACAGC CFA 20: 31,487,071-31,487,165 CFA 20: 46 Mb TTCTCTTCTCAGGCCGTTCCGTTT CFA 20: 46,772,055-46,772,149 CFA 31: 16 Mb TGTGAGATTTGCATTACATGACCCTGGA CFA 31: 16,758,969-16,759,073

TABLE 3 Genomic regions significantly different between mut-KIT tumors and wt-KIT tumors Freq Freq in mut- in wt- KIT KIT Diff Region Loc Event Length (%) (%) (%) p-value q-bound Gene Symbols chr5: 0-133,326 q11 Loss 133,326 66.7 13.2 53.5 5.26E−08 5.71E−07 cOR9R4, LOC102155730 chr5: 133,326-381,054 q11 Loss 247,728 60.6 0.0 60.6 1.59E−13 1.60E−11 LOC102156207, LOC102155804 chr5: 381,054-415,492 q11 Loss 34,438 60.6 1.3 59.3 2.88E−12 1.11E−10 chr5: 415,492-431,007 q11 Loss 15,515 63.6 5.3 58.4 1.67E−10 2.84E−09 chr5: 431,007-481,597 q11 Loss 50,590 66.7 7.9 58.8 5.39E−10 8.10E−09 chr5: 481,597-571,794 q11 Loss 90,197 69.7 7.9 61.8 9.19E−11 1.85E−09 LOC102156249, LOC102156294 chr5: 571,794-589,240 q11 Loss 17,446 69.7 9.2 60.5 3.47E−10 5.61E−09 chr5: 589,240-599,748 q11 Loss 10,508 69.7 10.5 59.2 1.18E−09 1.59E−08 chr5: 599,748-626,252 q11 Loss 26,504 63.6 10.5 53.1 3.13E−08 3.41E−07 chr5: 626,252-650,764 q11 Loss 24,512 63.6 13.2 50.5 2.37E−07 2.43E−06 chr5: 650,764-749,787 q11 Loss 99,023 66.7 14.5 52.2 1.36E−07 1.40E−06 LOC100687391, LOC102155966, B3GAT1 chr5: 754,021-761,056 q11 Loss 7,035 63.6 7.9 55.7 2.91E−09 3.74E−08 GLB1L2 chr5: 761,056-769,157 q11 Loss 8,101 63.6 0.0 63.6 2.33E−14 6.04E−12 GLB1L2 chr5: 769,157-774,383 q11 Loss 5,226 60.6 0.0 60.6 1.59E−13 1.60E−11 GLB1L2 chr5: 774,383-880,302 q11 Loss 105,919 63.6 0.0 63.6 2.33E−14 6.04E−12 GLB1L2, GLB1L3, LOC102156417, LOC607396 chr5: 880,302-1,142,528 q11 Loss 262,226 60.6 0.0 60.6 1.59E−13 1.60E−11 LOC102156417, LOC607396, LOC102156118, ACAD8, THYN1, VPS26B, NCAPD3, JAM3 chr5: 1,142,528-1,153,946 q11 Loss 11,418 60.6 1.3 59.3 2.88E−12 1.11E−10 JAM3 chr5: 1,153,946-1,167,298 q11 Loss 13,352 60.6 2.6 58.0 2.72E−11 6.21E−10 JAM3 chr5: 1,167,298-1,182,071 q11 Loss 14,773 57.6 2.6 54.9 1.56E−10 2.67E−09 chr5: 1,182,071-1,211,221 q11 Loss 29,150 60.6 6.6 54.0 3.94E−09 4.98E−08 chr5: 1,211,221-1,224,769 q11 Loss 13,548 60.6 7.9 52.7 1.46E−08 1.64E−07 chr5: 1,224,769-1,333,535 q11 Loss 108,766 60.6 10.5 50.1 1.42E−07 1.46E−06 IGSF9B chr5: 1,333,535-1,360,338 q11 Loss 26,803 60.6 7.9 52.7 1.46E−08 1.64E−07 SPATA19 chr5: 1,360,338-1,404,082 q11 Loss 43,744 60.6 1.3 59.3 2.88E−12 1.11E−10 SPATA19 chr5: 1,404,082-1,491,974 q11 Loss 87,892 57.6 1.3 56.3 1.75E−11 4.38E−10 chr5: 1,491,974-1,776,954 q11 Loss 284,980 57.6 0.0 57.6 1.02E−12 5.07E−11 chr5: 1,776,954-1,922,199 q11 Loss 145,245 57.6 1.3 56.3 1.75E−11 4.38E−10 chr5: 1,922,199-2,400,485 q11 Loss 478,286 57.6 0.0 57.6 1.02E−12 5.07E−11 OPCML chr5: 2,400,485-2,584,033 q11 Loss 183,548 54.5 0.0 54.5 6.22E−12 1.92E−10 OPCML chr5: 2,584,033-2,704,511 q11 Loss 120,478 57.6 0.0 57.6 1.02E−12 5.07E−11 OPCML chr5: 2,704,511-3,349,181 q11 Loss 644,670 54.5 0.0 54.5 6.22E−12 1.92E−10 NTM chr5: 3,349,181-3,399,027 q11 Loss 49,846 57.6 0.0 57.6 1.02E−12 5.07E−11 NTM chr5: 3,399,027-3,433,371 q11 Loss 34,344 63.6 1.3 62.3 4.46E−13 3.02E−11 NTM chr5: 3,433,371-3,464,227 q11 Loss 30,856 63.6 2.6 61.0 4.45E−12 1.48E−10 NTM chr5: 3,464,227-3,499,187 q11 Loss 34,960 69.7 5.3 64.4 4.36E−12 1.48E−10 NTM chr5: 3,499,187-3,576,535 q11 Loss 77,348 72.7 5.3 67.5 6.18E−13 3.91E−11 NTM chr5: 3,576,535-3,690,776 q11 Loss 114,241 69.7 5.3 64.4 4.36E−12 1.48E−10 NTM, LOC102154392, LOC102154472 chr5: 3,690,776-3,710,651 q11 Loss 19,875 69.7 2.6 67.1 9.50E−14 1.12E−11 chr5: 3,710,651-3,753,162 q11 Loss 42,511 69.7 1.3 68.4 8.56E−15 3.54E−12 chr5: 3,753,162-3,807,807 q11 Loss 54,645 66.7 0.0 66.7 3.18E−15 3.41E−12 chr5: 3,807,807-4,031,844 q11-q12 Loss 224,037 57.6 0.0 57.6 1.02E−12 5.07E−11 NCAPD3 chr5: 4,031,844-4,721,545 q12 Loss 689,701 54.5 0.0 54.5 6.22E−12 1.92E−10 NCAPD3, SNX19, LOC102151125, ADAMTS15, ADAMTS8, ZBTB44, ST14, APLP2 chr5: 4,721,545-5,115,444 q12 Loss 393,899 57.6 0.0 57.6 1.02E−12 5.07E−11 APLP2, LOC102154549, PRDM10, NFRKB, TMEM45B chr5: 5,115,444-5,377,336 q12 Loss 261,892 60.6 0.0 60.6 1.59E−13 1.60E−11 BARX2, LOC102152251, LOC102156865 chr5: 5,377,336-5,667,233 q12 Loss 289,897 57.6 0.0 57.6 1.02E−12 5.07E−11 LOC102156865, ARHGAP32 chr5: 5,667,233-5,821,501 q12 Loss 154,268 60.6 0.0 60.6 1.59E−13 1.60E−11 ARHGAP32, KCNJ5, KCNJ1 chr5: 5,821,501-5,854,005 q12 Loss 32,504 57.6 0.0 57.6 1.02E−12 5.07E−11 FLI1 chr5: 5,854,005-5,867,075 q12 Loss 13,070 54.5 0.0 54.5 6.22E−12 1.92E−10 FLI1 chr5: 5,867,075-5,926,290 q12 Loss 59,215 51.5 0.0 51.5 3.58E−11 7.73E−10 FLI1 chr5: 6,218,480-6,467,492 q12 Loss 249,012 54.5 0.0 54.5 6.22E−12 1.92E−10 LOC102153240 chr5: 6,467,492-6,667,635 q12 Loss 200,143 57.6 0.0 57.6 1.02E−12 5.07E−11 chr5: 6,667,635-6,817,010 q12 Loss 149,375 54.5 0.0 54.5 6.22E−12 1.92E−10 chr5: 6,817,010-6,908,015 q12 Loss 91,005 57.6 0.0 57.6 1.02E−12 5.07E−11 chr5: 6,908,015-7,040,574 q12 Loss 132,559 54.5 0.0 54.5 6.22E−12 1.92E−10 chr5: 7,040,574-7,122,705 q12 Loss 82,131 51.5 0.0 51.5 3.58E−11 7.73E−10 chr5: 7,440,761-7,479,883 q12 Loss 39,122 51.5 0.0 51.5 3.58E−11 7.73E−10 chr5: 7,479,883-7,489,731 q12 Loss 9,848 57.6 0.0 57.6 1.02E−12 5.07E−11 chr5: 7,489,731-7,693,556 q12 Loss 203,825 60.6 1.3 59.3 2.88E−12 1.11E−10 KIRREL3 chr5: 7,693,556-7,918,502 q12 Loss 224,946 57.6 1.3 56.3 1.75E−11 4.38E−10 KIRREL3 chr5: 7,918,502-7,937,124 q12 Loss 18,622 57.6 3.9 53.6 9.72E−10 1.34E−08 KIRREL3 chr5: 7,937,124-7,968,162 q12 Loss 31,038 60.6 3.9 56.7 1.79E−10 2.97E−09 KIRREL3 chr5: 7,968,162-8,094,810 q12 Loss 126,648 60.6 7.9 52.7 1.46E−08 1.64E−07 KIRREL3 chr5: 8,113,294-8,135,481 q12 Loss 22,187 57.6 2.6 54.9 1.56E−10 2.67E−09 KIRREL3, ST3GAL4 chr5: 8,135,481-8,151,341 q12 Loss 15,860 57.6 0.0 57.6 1.02E−12 5.07E−11 ST3GAL4 chr5: 8,592,141-8,608,158 q12 Loss 16,017 54.5 0.0 54.5 6.22E−12 1.92E−10 PUS3, HYLS1 chr5: 8,608,158-9,046,592 q12 Loss 438,434 60.6 0.0 60.6 1.59E−13 1.60E−11 PATE2, PATE1, ACRV1, CHEK1, STT3A, EI24, FEZ1 chr5: 9,046,592-9,062,538 q12 Loss 15,946 57.6 0.0 57.6 1.02E−12 5.07E−11 PKNOX2 chr5: 9,062,538-9,218,872 q12 Loss 156,334 60.6 0.0 60.6 1.59E−13 1.60E−11 PKNOX2, LOC102156272 chr5: 9,218,872-9,395,688 q12 Loss 176,816 57.6 0.0 57.6 1.02E−12 5.07E−11 PKN0X2, LOC102156272, TMEM218, SLC37A2 chr5: 9,395,688-9,670,771 q12 Loss 275,083 54.5 0.0 54.5 6.22E−12 1.92E−10 SLC37A2, CCDC15, HEPACAM, HEPN1, ROBO4, ROBO3, MSANTD2, ESAM, VSIG2 chr5: 9,670,771-9,837,421 q12 Loss 166,650 66.7 0.0 66.7 3.18E−15 3.41E−12 VSIG2, NRGN, SPA17, SIAE, TBRG1, PANX3, OR8A1 chr5: 9,837,421-10,291,526 q12 Loss 454,105 63.6 0.0 63.6 2.33E−14 6.04E−12 LOC489314, LOC489315, LOC100686868, LOC489317, OR8B4, cOR8C4, LOC100687027, LOC609966, cOR8B15, LOC100686309, LOC100686560, LOC100687264, LOC100687028, LOC489325, LOC100687427, LOC489326, OR8D2, LOC100687661, LOC102151657 chr5: 10,291,526-10,518,964 q12 Loss 227,438 60.6 0.0 60.6 1.59E−13 1.60E−11 OR28D07, OR16A12, OR08E10, VWA5A, LOC100688048, LOC100688253, cOR10D5P, LOC489338, LOC489340, LOC489341, cOR10G11, OR10S1 chr5: 10,518,964-10,717,556 q12 Loss 198,592 60.6 1.3 59.3 2.88E−12 1.11E−10 cOR8C5, OR4D5, TMEM225, LOC100688741, LOC489347, cOR6M4, cOR6M5, OR6M1, OR6X1, ZNF202 chr5: 10,717,556-10,824,977 q12 Loss 107,421 66.7 1.3 65.4 6.42E−14 9.66E−12 ZNF202, LOC102155174, SCN3B, GRAMD1B chr5: 10,824,977-10,867,242 q12 Loss 42,265 69.7 1.3 68.4 8.56E−15 3.54E−12 GRAMD1B chr5: 10,867,242-10,929,698 q12 Loss 62,456 63.6 1.3 62.3 4.46E−13 3.02E−11 GRAMD1B chr5: 10,929,698-11,178,647 q12 Loss 248,949 57.6 1.3 56.3 1.75E−11 4.38E−10 GRAMD1B, LOC609517 chr5: 11,178,647-11,307,095 q12-q13 Loss 128,448 60.6 1.3 59.3 2.88E−12 1.11E−10 FOXRED1, CLMP, HSPA8 chr5: 11,307,095-11,365,230 q13 Loss 58,135 63.6 1.3 62.3 4.46E−13 3.02E−11 chr5: 11,365,230-11,372,266 q13 Loss 7,036 57.6 1.3 56.3 1.75E−11 4.38E−10 BSX chr5: 11,372,266-11,378,881 q13 Loss 6,615 54.5 1.3 53.2 9.97E−11 1.85E−09 chr5: 11,378,881-11,425,343 q13 Loss 46,462 54.5 2.6 51.9 8.40E−10 1.18E−08 C5H11orf63 chr5: 11,616,512-11,647,820 q13 Loss 31,308 54.5 2.6 51.9 8.40E−10 1.18E−08 chr5: 11,647,820-11,670,962 q13 Loss 23,142 57.6 2.6 54.9 1.56E−10 2.67E−09 chr5: 11,670,962-11,842,755 q13 Loss 171,793 60.6 2.6 58.0 2.72E−11 6.21E−10 chr5: 11,842,755-11,941,764 q13 Loss 99,009 63.6 2.6 61.0 4.45E−12 1.48E−10 LOC100682870 chr5: 11,941,764-12,064,197 q13 Loss 122,433 60.6 2.6 58.0 2.72E−11 6.21E−10 LOC100682870 chr5: 12,064,197-12,133,064 q13 Loss 68,867 63.6 2.6 61.0 4.45E−12 1.48E−10 BLID, LOC102156815 chr5: 12,133,064-12,216,348 q13 Loss 83,284 66.7 3.9 62.7 4.94E−12 1.59E−10 LOC102156815 chr5: 12,216,348-12,272,269 q13 Loss 55,921 63.6 3.9 59.7 3.09E−11 6.77E−10 chr5: 12,272,269-12,353,441 q13 Loss 81,172 66.7 3.9 62.7 4.94E−12 1.59E−10 chr5: 12,353,441-12,383,117 q13 Loss 29,676 69.7 3.9 65.7 7.29E−13 4.00E−11 chr5: 12,383,117-12,432,341 q13 Loss 49,224 60.6 3.9 56.7 1.79E−10 2.97E−09 chr5: 12,432,341-12,543,948 q13 Loss 111,607 57.6 2.6 54.9 1.56E−10 2.67E−09 SORL1 chr5: 12,543,948-12,959,155 q13 Loss 415,207 60.6 2.6 58.0 2.72E−11 6.21E−10 SORL1, SC5D, LOC102157382, LOC489316, TECTA chr5: 12,959,155-13,022,344 q13 Loss 63,189 66.7 2.6 64.0 6.76E−13 3.91E−11 cOR8B3, TBCEL chr5: 13,022,344-13,134,971 q13 Loss 112,627 63.6 2.6 61.0 4.45E−12 1.48E−10 TBCEL, cOR8B1P, GRIK4 chr5: 13,134,971-13,672,471 q13 Loss 537,500 66.7 2.6 64.0 6.76E−13 3.91E−11 cOR8D5, GRIK4, OR08H04, LOC607862, ARHGEF12 chr5: 13,672,471-13,833,005 q13 Loss 160,534 66.7 1.3 65.4 6.42E−14 9.66E−12 TMEM136, POU2F3, OAF, LOC100683683 chr5: 13,833,005-14,058,393 q13 Loss 225,388 63.6 1.3 62.3 4.46E−13 3.02E−11 TRIM29 chr5: 14,058,393-14,123,666 q13 Loss 65,273 63.6 2.6 61.0 4.45E−12 1.48E−10 chr5: 14,123,666-14,157,781 q13 Loss 34,115 63.6 3.9 59.7 3.09E−11 6.77E−10 chr5: 14,157,781-14,194,514 q13 Loss 36,733 63.6 5.3 58.4 1.67E−10 2.84E−09 chr5: 14,194,514-14,268,931 q13 Loss 74,417 66.7 6.6 60.1 1.33E−10 2.43E−09 PVRL1 chr5: 14,268,931-14,301,979 q13 Loss 33,048 69.7 6.6 63.1 2.16E−11 5.35E−10 PVRL1 chr5: 14,301,979-14,350,047 q13 Loss 48,068 69.7 7.9 61.8 9.19E−11 1.85E−09 chr5: 14,350,047-14,434,923 q13 Loss 84,876 69.7 6.6 63.1 2.16E−11 5.35E−10 LOC102152657 chr5: 14,434,923-14,497,888 q13 Loss 62,965 63.6 2.6 61.0 4.45E−12 1.48E−10 LOC102152657 chr5: 14,497,888-14,560,969 q13 Loss 63,081 60.6 2.6 58.0 2.72E−11 6.21E−10 LOC102152657, THY1, LOC102152850, USP2, MFRP chr5: 14,691,773-14,813,785 q13 Loss 122,012 54.5 2.6 51.9 8.40E−10 1.18E−08 PDZD3, NLRX1, ABCG4, HINFP, C2CD2L, DPAGT1, LOC489372, HMBS, VPS11, LOC102154262, HYOU1 chr5: 14,813,785-15,004,440 q13 Loss 190,655 57.6 2.6 54.9 1.56E−10 2.67E−09 LOC102152874, SLC37A4, TRAPPC4, RPS25, CCDC84, FOXR1, UPK2, BCL9L, CXCR5, DDX6 chr5: 15,004,440-15,224,520 q13 Loss 220,080 60.6 2.6 58.0 2.72E−11 6.21E−10 DDX6, LOC102155606, LOC102155677, TREH, PHLDB1, ARCN1, IFT46, TMEM25, TTC36, KMT2A chr5: 15,517,999-15,590,459 q13 Loss 72,460 75.8 5.3 70.5 7.94E−14 1.12E−11 SCN2B, SCN4B, TMPRSS4 chr5: 15,590,459-15,629,393 q13-q14.1 Loss 38,934 69.7 5.3 64.4 4.36E−12 1.48E−10 TMPRSS4 chr5: 15,629,393-15,725,600 q14.1 Loss 96,207 72.7 5.3 67.5 6.18E−13 3.91E−11 IL10RA chr5: 15,725,600-15,902,742 q14.1 Loss 177,142 72.7 7.9 64.8 1.43E−11 4.38E−10 TMPRSS13, FXYD6, FXYD2, DSCAML1 chr5: 15,902,742-15,949,009 q14.1 Loss 46,267 72.7 10.5 62.2 2.01E−10 3.28E−09 DSCAML1 chr5: 15,949,009-16,029,118 q14.1 Loss 80,109 72.7 11.8 60.9 6.54E−10 9.79E−09 DSCAML1 chr5: 16,029,118-16,062,976 q14.1 Loss 33,858 69.7 11.8 57.9 3.69E−09 4.71E−08 DSCAML1 chr5: 16,062,976-16,105,095 q14.1 Loss 42,119 69.7 10.5 59.2 1.18E−09 1.59E−08 DSCAML1 chr5: 16,105,095-16,139,973 q14.1 Loss 34,878 66.7 6.6 60.1 1.33E−10 2.43E−09 DSCAML1 chr5: 16,139,973-16,160,653 q14.1 Loss 20,680 54.5 2.6 51.9 8.40E−10 1.18E−08 DSCAML1 chr5: 16,384,444-16,411,973 q14.1 Loss 27,529 54.5 2.6 51.9 8.40E−10 1.18E−08 PCSK7, TAGLN, SIDT2 chr5: 16,452,934-16,698,305 q14.1 Loss 245,371 54.5 2.6 51.9 8.40E−10 1.18E−08 SIK3 chr5: 16,698,305-16,809,673 q14.1 Loss 111,368 57.6 2.6 54.9 1.56E−10 2.67E−09 SIK3, APOA1, APOC3, APOA4, APOA5, ZNF259, BUD13 chr5: 16,809,673-16,821,033 q14.1 Loss 11,360 63.6 2.6 61.0 4.45E−12 1.48E−10 BUD13 chr5: 16,821,033-16,834,415 q14.1 Loss 13,382 66.7 2.6 64.0 6.76E−13 3.91E−11 LOC479430 chr5: 16,834,415-16,847,536 q14.1 Loss 13,121 69.7 2.6 67.1 9.50E−14 1.12E−11 LOC479430 chr5: 16,847,536-16,879,661 q14.1 Loss 32,125 72.7 3.9 68.8 9.86E−14 1.12E−11 LOC610527 chr5: 16,879,661-16,894,745 q14.1 Loss 15,084 72.7 5.3 67.5 6.18E−13 3.91E−11 LOC610527 chr5: 16,894,745-16,911,794 q14.1 Loss 17,049 72.7 6.6 66.1 3.20E−12 1.22E−10 chr5: 16,911,794-17,003,811 q14.1 Loss 92,017 72.7 7.9 64.8 1.43E−11 4.38E−10 chr5: 17,003,811-17,271,198 q14.1 Loss 267,387 81.8 7.9 73.9 2.80E−14 7.01E−12 chr5: 17,271,198-17,298,351 q14.1 Loss 27,153 75.8 3.9 71.8 1.21E−14 3.54E−12 q14.1 chr5: 17,298,351-17,399,902 q14.2 Loss 101,551 72.7 3.9 68.8 9.86E−14 1.12E−11 LOC102153762 chr5: 17,399,902-17,524,675 q14.2 Loss 124,773 69.7 3.9 65.7 7.29E−13 4.00E−11 LOC102153762 chr5: 17,524,675-17,736,000 q14.2 Loss 211,325 66.7 3.9 62.7 4.94E−12 1.59E−10 LOC102153809, LOC102153862, LOC102153906 chr5: 17,736,000-17,762,360 q14.2 Loss 26,360 66.7 2.6 64.0 6.76E−13 3.91E−11 chr5: 17,762,360-17,778,609 q14.2 Loss 16,249 57.6 2.6 54.9 1.56E−10 2.67E−09 chr5: 17,778,609-17,796,114 q14.2 Loss 17,505 54.5 2.6 51.9 8.40E−10 1.18E−08 chr5: 18,030,450-18,175,274 q14.2 Loss 144,824 63.6 2.6 61.0 4.45E−12 1.48E−10 CADM1 chr5: 18,175,274-18,276,940 q14.2 Loss 101,666 66.7 2.6 64.0 6.76E−13 3.91E−11 CADM1 chr5: 18,276,940-18,351,440 q14.2 Loss 74,500 69.7 2.6 67.1 9.50E−14 1.12E−11 NXPE4 chr5: 18,351,440-18,363,393 q14.2 Loss 11,953 69.7 3.9 65.7 7.29E−13 4.00E−11 NXPE4 chr5: 18,363,393-18,596,946 q14.2 Loss 233,553 72.7 3.9 68.8 9.86E−14 1.12E−11 NXPE4, SCN2B chr5: 18,596,946-18,657,666 q14.2-q14.3 Loss 60,720 54.5 3.9 50.6 4.94E−09 5.94E−08 NXPE4 chr5: 18,657,666-18,880,390 q14.3 Loss 222,724 54.5 2.6 51.9 8.40E−10 1.18E−08 NXPE4, FAM55A chr5: 19,010,922-19,060,287 q14.3 Loss 49,365 54.5 2.6 51.9 8.40E−10 1.18E−08 NNMT chr5: 19,060,287-19,074,393 q14.3 Loss 14,106 57.6 5.3 52.3 4.74E−09 5.74E−08 LOC489397 chr5: 19,074,393-19,270,731 q14.3 Loss 196,338 69.7 10.5 59.2 1.18E−09 1.59E−08 ZBTB16 chr5: 19,270,731-19,281,974 q14.3 Loss 11,243 75.8 10.5 65.2 3.08E−11 6.77E−10 ZBTB16 chr5: 19,281,974-19,336,090 q14.3 Loss 54,116 78.8 11.8 66.9 1.49E−11 4.38E−10 ZBTB16, LOC102155430, HTR3A chr5: 19,336,090-19,348,097 q14.3 Loss 12,007 75.8 7.9 67.9 2.01E−12 9.88E−11 HTR3A chr5: 19,348,097-19,522,336 q14.3 Loss 174,239 69.7 2.6 67.1 9.50E−14 1.12E−11 HTR3B, SIDT2, USP28, CLDN25, ZW10 chr5: 19,522,336-19,761,676 q14.3 Loss 239,340 66.7 2.6 64.0 6.76E−13 3.91E−11 ZW10, TMPRSS5, LOC102155912, DRD2 chr5: 19,761,676-19,772,292 q14.3 Loss 10,616 54.5 1.3 53.2 9.97E−11 1.85E−09 DRD2 chr5: 19,772,292-19,798,108 q14.3 Loss 25,816 51.5 1.3 50.2 5.38E−10 8.10E−09 DRD2 chr5: 19,870,693-19,882,469 q14.3 Loss 11,776 54.5 1.3 53.2 9.97E−11 1.85E−09 TTC12 chr5: 19,882,469-19,890,567 q14.3 Loss 8,098 57.6 1.3 56.3 1.75E−11 4.38E−10 chr5: 19,890,567-19,914,466 q14.3 Loss 23,899 60.6 1.3 59.3 2.88E−12 1.11E−10 NCAM1 chr5: 19,914,466-19,937,434 q14.3 Loss 22,968 63.6 1.3 62.3 4.46E−13 3.02E−11 NCAM1 chr5: 19,937,434-19,990,331 q14.3 Loss 52,897 63.6 2.6 61.0 4.45E−12 1.48E−10 NCAM1 chr5: 19,990,331-20,005,792 q14.3 Loss 15,461 66.7 2.6 64.0 6.76E−13 3.91E−11 NCAM1 chr5: 20,005,792-20,095,665 q14.3 Loss 89,873 63.6 2.6 61.0 4.45E−12 1.48E−10 NCAM1 chr5: 20,095,665-20,101,537 q14.3 Loss 5,872 57.6 2.6 54.9 1.56E−10 2.67E−09 NCAM1 chr5: 20,101,537-20,117,741 q14.3 Loss 16,204 54.5 2.6 51.9 8.40E−10 1.18E−08 NCAM1 chr5: 20,530,275-20,593,191 q14.3 Loss 62,916 57.6 2.6 54.9 1.56E−10 2.67E−09 chr5: 20,593,191-20,741,425 q14.3 Loss 148,234 60.6 2.6 58.0 2.72E−11 6.21E−10 chr5: 20,741,425-20,763,660 q14.3 Loss 22,235 63.6 2.6 61.0 4.45E−12 1.48E−10 chr5: 20,763,660-20,903,254 q14.3 Loss 139,594 69.7 2.6 67.1 9.50E−14 1.12E−11 C5H11orf34 chr5: 20,903,254-20,930,927 q14.3 Loss 27,673 66.7 2.6 64.0 6.76E−13 3.91E−11 PTS, BCO2 chr5: 20,930,927-20,965,652 q14.3 Loss 34,725 60.6 2.6 58.0 2.72E−11 6.21E−10 BCO2 chr5: 20,965,652-21,478,736 q14.3 Loss 513,084 57.6 2.6 54.9 1.56E−10 2.67E−09 TEX12, IL18, SDHD, TIMM8B, C5H11orf57, PIH1D2, DLAT, DIXDC1, C5H11orf52, HSPB2, CRYAB, C5H11orf1, FDXACB1, ALG9, PPP2R1B, SIK2 chr5: 21,478,736-21,531,994 q14.3 Loss 53,258 54.5 2.6 51.9 8.40E−10 1.18E−08 SIK2, LOC102153351, LAYN chr5: 21,592,851-21,655,541 q14.3 Loss 62,690 54.5 2.6 51.9 8.40E−10 1.18E−08 LOC102153632 chr5: 21,655,541-21,691,724 q14.3 Loss 36,183 57.6 2.6 54.9 1.56E−10 2.67E−09 POU2AF1 chr5: 21,691,724-21,842,858 q14.3 Loss 151,134 60.6 2.6 58.0 2.72E−11 6.21E−10 POU2AF1, LOC102153661, COLCA2, C5H11orf53 chr5: 21,842,858-21,939,169 q14.3 Loss 96,311 57.6 2.6 54.9 1.56E−10 2.67E−09 LOC611159 chr5: 21,939,169-22,200,189 q14.3 Loss 261,020 54.5 2.6 51.9 8.40E−10 1.18E−08 LOC611159, LOC100685611 chr5: 22,200,189-22,469,863 q14.3 Loss 269,674 57.6 2.6 54.9 1.56E−10 2.67E−09 ARHGAP20, LOC102154781, FDX1 chr5: 22,469,863-22,525,617 q14.3 Loss 55,754 54.5 2.6 51.9 8.40E−10 1.18E−08 FDX1 chr5: 22,525,617-22,713,579 q14.3 Loss 187,962 57.6 2.6 54.9 1.56E−10 2.67E−09 RDX, ZC3H12C chr5: 22,713,579-22,756,080 q14.3 Loss 42,501 60.6 2.6 58.0 2.72E−11 6.21E−10 ZC3H12C, LOC102154417 chr5: 22,756,080-22,882,299 q14.3 Loss 126,219 66.7 2.6 64.0 6.76E−13 3.91E−11 chr5: 22,882,299-22,956,725 q14.3 Loss 74,426 60.6 2.6 58.0 2.72E−11 6.21E−10 chr5: 22,956,725-23,730,600 q14.3 Loss 773,875 57.6 2.6 54.9 1.56E−10 2.67E−09 C5H11orf87, DDX10 chr5: 23,730,600-24,006,504 q14.3 Loss 275,904 63.6 2.6 61.0 4.45E−12 1.48E−10 DDX10 chr5: 24,006,504-24,225,138 q14.3 Loss 218,634 60.6 2.6 58.0 2.72E−11 6.21E−10 EXPH5, KDELC2, C5H11orf65, ATM chr5: 24,225,138-24,750,152 q14.3 Loss 525,014 57.6 2.6 54.9 1.56E−10 2.67E−09 ATM, NPAT, ACAT1, LOC102156093, CUL5, RAB39, SLC35F2, SLN chr5: 25,055,292-25,232,639 q14.3 Loss 177,347 51.5 1.3 50.2 5.38E−10 8.10E−09 CWF19L2, LOC102157218 chr5: 25,232,639-25,250,829 q14.3 Loss 18,190 54.5 1.3 53.2 9.97E−11 1.85E−09 chr5: 25,250,829-25,684,529 q14.3 Loss 433,700 63.6 3.9 59.7 3.09E−11 6.77E−10 GUCY1A2 chr5: 25,684,529-25,836,174 q14.3 Loss 151,645 60.6 3.9 56.7 1.79E−10 2.97E−09 chr5: 25,836,174-26,006,165 q14.3 Loss 169,991 54.5 3.9 50.6 4.94E−09 5.94E−08 chr5: 26,006,165-26,253,762 q14.3 Loss 247,597 57.6 3.9 53.6 9.72E−10 1.34E−08 AASDHPPT, KBTBD3, MSANTD4 chr5: 26,253,762-26,630,899 q14.3 Loss 377,137 57.6 2.6 54.9 1.56E−10 2.67E−09 MSANTD4, LOC100688413, GRIA4 chr5: 26,630,899-26,803,690 q14.3 Loss 172,791 63.6 3.9 59.7 3.09E−11 6.77E−10 GRIA4 chr5: 26,803,690-26,813,706 q14.3 Loss 10,016 60.6 3.9 56.7 1.79E−10 2.97E−09 chr5: 26,813,706-26,911,584 q14.3 Loss 97,878 54.5 2.6 51.9 8.40E−10 1.18E−08 chr5: 26,911,584-27,034,403 q14.3 Loss 122,819 54.5 1.3 53.2 9.97E−11 1.85E−09 chr5: 27,034,403-27,145,741 q14.3 Loss 111,338 60.6 1.3 59.3 2.88E−12 1.11E−10 CASP4 chr5: 27,145,741-27,306,771 q14.3-q21 Loss 161,030 60.6 2.6 58.0 2.72E−11 6.21E−10 CASP4, CASP12, LOC479459 chr5: 27,306,771-27,464,906 q21 Loss 158,135 57.6 2.6 54.9 1.56E−10 2.67E−09 chr5: 27,464,906-27,526,152 q21 Loss 61,246 60.6 2.6 58.0 2.72E−11 6.21E−10 chr5: 27,526,152-27,671,544 q21 Loss 145,392 60.6 3.9 56.7 1.79E−10 2.97E−09 chr5: 27,671,544-27,702,732 q21 Loss 31,188 54.5 2.6 51.9 8.40E−10 1.18E−08 chr5: 27,919,757-27,934,987 q21 Loss 15,230 54.5 2.6 51.9 8.40E−10 1.18E−08 PDGFD chr5: 27,934,987-27,963,780 q21 Loss 28,793 57.6 3.9 53.6 9.72E−10 1.34E−08 PDGFD chr5: 27,963,780-27,990,233 q21 Loss 26,453 63.6 3.9 59.7 3.09E−11 6.77E−10 DDI1, PDGFD chr5: 27,990,233-28,106,271 q21 Loss 116,038 66.7 3.9 62.7 4.94E−12 1.59E−10 PDGFD chr5: 28,106,271-28,202,681 q21 Loss 96,410 57.6 2.6 54.9 1.56E−10 2.67E−09 chr5: 28,202,681-28,394,367 q21 Loss 191,686 60.6 2.6 58.0 2.72E−11 6.21E−10 DYNC2H1 chr5: 28,394,367-28,568,040 q21 Loss 173,673 63.6 2.6 61.0 4.45E−12 1.48E−10 DYNC2H1 chr5: 28,568,040-28,652,646 q21 Loss 84,606 60.6 2.6 58.0 2.72E−11 6.21E−10 DYNC2H1 chr5: 28,652,646-28,704,136 q21 Loss 51,490 57.6 2.6 54.9 1.56E−10 2.67E−09 DYNC2H1 chr5: 28,704,136-28,805,065 q21 Loss 100,929 54.5 1.3 53.2 9.97E−11 1.85E−09 DYNC2H1, DCUN1D5, MMP13 chr5: 28,805,065-28,959,058 q21 Loss 153,993 57.6 1.3 56.3 1.75E−11 4.38E−10 MMP13, MMP12, MMP3 chr5: 28,959,058-29,015,567 q21 Loss 56,509 60.6 1.3 59.3 2.88E−12 1.11E−10 LOC489428 chr5: 29,015,567-29,261,694 q21 Loss 246,127 57.6 1.3 56.3 1.75E−11 4.38E−10 MMP8, MMP27, MMP20, MMP7, LOC100856041 chr5: 29,261,694-29,285,265 q21 Loss 23,571 54.5 1.3 53.2 9.97E−11 1.85E−09 LOC100856041 chr5: 29,285,265-29,647,998 q21 Loss 362,733 51.5 1.3 50.2 5.38E−10 8.10E−09 LOC102154369, BIRC2, BIRC3, LOC102153712, YAP1, LOC102153832, C5H11orf70, KIAA1377 chr5: 29,647,998-29,926,991 q21 Loss 278,993 54.5 1.3 53.2 9.97E−11 1.85E−09 KIAA1377, ANGPTL5, TRPC6 chr5: 29,926,991-29,943,628 q21 Loss 16,637 63.6 2.6 61.0 4.45E−12 1.48E−10 TRPC6 chr5: 29,943,628-30,117,141 q21 Loss 173,513 66.7 2.6 64.0 6.76E−13 3.91E−11 TRPC6 chr5: 30,117,141-30,292,482 q21 Loss 175,341 69.7 2.6 67.1 9.50E−14 1.12E−11 ZZEF1, CYB5D2, ANKFY1 chr5: 30,292,482-30,412,787 q21 Loss 120,305 72.7 2.6 70.1 1.22E−14 3.54E−12 ANKFY1, UBE2G1 chr5: 30,412,787-30,460,632 q21 Loss 47,845 66.7 2.6 64.0 6.76E−13 3.91E−11 chr5: 30,460,632-30,475,453 q21 Loss 14,821 69.7 9.2 60.5 3.47E−10 5.61E−09 SPNS3 chr5: 30,475,453-30,505,927 q21 Loss 30,474 69.7 10.5 59.2 1.18E−09 1.59E−08 SPNS3, SPNS2 chr5: 30,505,927-30,624,645 q21 Loss 118,718 69.7 13.2 56.5 1.07E−08 1.25E−07 SPNS2, MYBBP1A, GGT6, LOC102155027, SMTNL2, TEKT1, FBXO39 chr5: 30,624,645-30,629,434 q21 Loss 4,789 75.8 14.5 61.3 9.65E−10 1.34E−08 XAF1 chr5: 30,629,434-30,839,322 q21 Loss 209,888 75.8 17.1 58.7 6.96E−09 8.23E−08 XAF1, SLC13A5, MED31, TXNDC17, KIAA0753, LOC102156039, PITPNM3, FAM64A, AIPL1 chr5: 30,839,322-30,858,577 q21 Loss 19,255 78.8 18.4 60.4 3.01E−09 3.86E−08 chr5: 30,858,577-30,879,260 q21 Loss 20,683 78.8 19.7 59.1 1.06E−08 1.24E−07 chr5: 30,879,260-30,944,859 q21 Loss 65,599 81.8 21.1 60.8 3.71E−09 4.73E−08 LOC102156228, LOC102156296 chr5: 30,944,859-30,994,655 q21 Loss 49,796 81.8 22.4 59.4 7.25E−09 8.55E−08 LOC102156392, LOC102156440 chr5: 30,994,655-31,066,726 q21 Loss 72,071 81.8 23.7 58.1 2.51E−08 2.75E−07 WSCD1 chr5: 31,066,726-31,080,498 q21 Loss 13,772 81.8 25.0 56.8 3.93E−08 4.27E−07 chr5: 31,096,931-31,168,215 q21 Loss 71,284 72.7 15.8 56.9 1.47E−08 1.65E−07 chr5: 31,168,215-31,189,337 q21 Loss 21,122 69.7 15.8 53.9 7.35E−08 7.77E−07 chr5: 31,189,337-31,203,739 q21 Loss 14,402 72.7 15.8 56.9 1.47E−08 1.65E−07 chr5: 31,203,739-31,220,304 q21 Loss 16,565 72.7 14.5 58.3 5.56E−09 6.58E−08 chr5: 31,220,304-31,307,195 q21 Loss 86,891 69.7 5.3 64.4 4.36E−12 1.48E−10 NLRP1 chr5: 31,307,195-31,369,071 q21 Loss 61,876 69.7 3.9 65.7 7.29E−13 4.00E−11 LOC100688504, MIS12, DERL2, DHX33, C1QBP, RPAIN, NUP88 chr5: 31,369,071-31,385,315 q21 Loss 16,244 72.7 3.9 68.8 9.86E−14 1.12E−11 NUP88 chr5: 31,385,315-31,404,096 q21 Loss 18,781 69.7 3.9 65.7 7.29E−13 4.00E−11 NUP88, RABEP1 chr5: 31,404,096-31,417,175 q21 Loss 13,079 69.7 2.6 67.1 9.50E−14 1.12E−11 RABEP1 chr5: 31,417,175-31,430,511 q21 Loss 13,336 72.7 2.6 70.1 1.22E−14 3.54E−12 RABEP1 chr5: 31,430,511-31,535,668 q21 Loss 105,157 69.7 1.3 68.4 8.56E−15 3.54E−12 RABEP1, SCIMP, ZFP3 chr5: 31,535,668-31,580,505 q21 Loss 44,837 66.7 1.3 65.4 6.42E−14 9.66E−12 SCIMP, ZFP3 chr5: 31,580,505-31,601,294 q21 Loss 20,789 60.6 1.3 59.3 2.88E−12 1.11E−10 ZFP3 chr5: 31,601,294-31,622,778 q21 Loss 21,484 60.6 2.6 58.0 2.72E−11 6.21E−10 KIF1C chr5: 31,622,778-31,685,371 q21 Loss 62,593 57.6 2.6 54.9 1.56E−10 2.67E−09 KIF1C, INCA1, CAMTA2, SPAG7, ENO3, PFN1, RNF167, SLC25A11, GP1BA chr5: 31,685,371-31,700,554 q21 Loss 15,183 60.6 2.6 58.0 2.72E−11 6.21E−10 chr5: 31,700,554-31,712,954 q21 Loss 12,400 60.6 3.9 56.7 1.79E−10 2.97E−09 CHRNE, C5H17orf107, MINK1 chr5: 31,712,954-31,739,657 q21 Loss 26,703 66.7 3.9 62.7 4.94E−12 1.59E−10 MINK1 chr5: 31,739,657-31,749,780 q21 Loss 10,123 66.7 2.6 64.0 6.76E−13 3.91E−11 MINK1 chr5: 31,749,780-31,910,093 q21 Loss 160,313 66.7 1.3 65.4 6.42E−14 9.66E−12 MINK1, PLD2, PSMB6, GLTPD2, VMO1, TM4SF5, ZMYND15, CXCL16, MED11, ARRB2, PELP1, ALOX15 chr5: 31,910,093-32,088,235 q21 Loss 178,142 66.7 2.6 64.0 6.76E−13 3.91E−11 LOC607567, LOC102155409, ALOX12, RNASEK, C5H17orf49, BCL6B, SLC16A13, SLC16A11, LOC102156250 chr5: 32,088,235-32,106,863 q21 Loss 18,628 66.7 7.9 58.8 5.39E−10 8.10E−09 CLEC10A chr5: 32,106,863-32,183,648 q21 Loss 76,785 69.7 11.8 57.9 3.69E−09 4.71E−08 ASGR2, ASGR1, DLG4 chr5: 32,411,753-32,430,833 q21 Loss 19,080 57.6 1.3 56.3 1.75E−11 4.38E−10 POLR2A chr5: 32,430,833-32,545,540 q21 Loss 114,707 60.6 1.3 59.3 2.88E−12 1.11E−10 LOC102153734, TNFSF12, TNFSF13, SENP3, EIF4A1, CD68, MPDU1, SOX15, FXR2, SAT2, SHBG chr5: 32,545,540-32,659,365 q21 Loss 113,825 60.6 2.6 58.0 2.72E−11 6.21E−10 ATP1B2, TP53, WRAP53, EFNB3, DNAH2 chr5: 32,659,365-32,708,735 q21 Loss 49,370 57.6 2.6 54.9 1.56E−10 2.67E−09 DNAH2, LOC102155246, KDM6B chr5: 32,708,735-32,807,894 q21-q22 Loss 99,159 57.6 1.3 56.3 1.75E−11 4.38E−10 KDM6B, TMEM88, NAA38, CYB5D1, LOC102155431, CHD3, LOC102155855, KCNAB3, TRAPPC1, CNTROB chr5: 32,807,894-32,866,241 q22 Loss 58,347 54.5 1.3 53.2 9.97E−11 1.85E−09 CNTROB, GUCY2D chr5: 32,866,241-32,905,477 q22 Loss 39,236 51.5 1.3 50.2 5.38E−10 8.10E−09 ALOX15B, ALOX12B chr5: 32,905,477-33,112,862 q22 Loss 207,385 54.5 1.3 53.2 9.97E−11 1.85E−09 ALOX12B, ALOXE3, HES7, PER1, VAMP2, LOC102157040, TMEM107, C5H17orf59, AURKB, CTC1, PFAS, RANGRF, SLC25A35, ARHGEF15 chr5: 33,112,862-33,421,296 q22 Loss 308,434 60.6 1.3 59.3 2.88E−12 1.11E−10 ODF4, KRBA2, RPL26, RNF222, NDEL1, MYH10 chr5: 33,421,296-33,693,288 q22 Loss 271,992 63.6 1.3 62.3 4.46E−13 3.02E−11 CCDC42, MFSD6L, PIK3R6, PIK3R5, LOC102155775, LOC102155856, NTN1 chr5: 33,693,288-33,705,238 q22 Loss 11,950 66.7 1.3 65.4 6.42E−14 9.66E−12 NTN1 chr5: 33,705,238-33,851,152 q22 Loss 145,914 69.7 3.9 65.7 7.29E−13 4.00E−11 NTN1 chr5: 33,851,152-33,989,233 q22 Loss 138,081 72.7 3.9 68.8 9.86E−14 1.12E−11 STX8 chr5: 33,989,233-34,071,975 q22 Loss 82,742 69.7 1.3 68.4 8.56E−15 3.54E−12 STX8 chr5: 34,071,975-34,105,056 q22 Loss 33,081 66.7 1.3 65.4 6.42E−14 9.66E−12 STX8, WDR16 chr5: 34,105,056-34,123,099 q22 Loss 18,043 63.6 1.3 62.3 4.46E−13 3.02E−11 WDR16 chr5: 34,123,099-34,198,416 q22 Loss 75,317 60.6 1.3 59.3 2.88E−12 1.11E−10 WDR16, USP43 chr5: 34,198,416-34,217,677 q22 Loss 19,261 60.6 0.0 60.6 1.59E−13 1.60E−11 chr5: 34,217,677-34,270,966 q22 Loss 53,289 57.6 0.0 57.6 1.02E−12 5.07E−11 DHRS7C, GLP2R chr5: 34,270,966-34,285,525 q22 Loss 14,559 60.6 1.3 59.3 2.88E−12 1.11E−10 GLP2R chr5: 34,285,525-34,386,603 q22 Loss 101,078 63.6 2.6 61.0 4.45E−12 1.48E−10 GLP2R, RCVRN, LOC479467, GAS7 chr5: 34,386,603-34,530,246 q22 Loss 143,643 60.6 1.3 59.3 2.88E−12 1.11E−10 LOC479467, GAS7 chr5: 34,530,246-34,682,629 q22 Loss 152,383 69.7 1.3 68.4 8.56E−15 3.54E−12 MYH13 chr5: 34,682,629-34,687,626 q22 Loss 4,997 72.7 2.6 70.1 1.22E−14 3.54E−12 chr5: 34,687,626-34,700,809 q22 Loss 13,183 75.8 2.6 73.1 1.43E−15 2.15E−12 MYH8 chr5: 34,700,809-34,733,618 q22 Loss 32,809 78.8 2.6 76.2 1.50E−16 1.13E−12 MYH8 chr5: 34,733,618-34,739,360 q22 Loss 5,742 72.7 2.6 70.1 1.22E−14 3.54E−12 chr5: 34,739,360-34,749,464 q22 Loss 10,104 66.7 2.6 64.0 6.76E−13 3.91E−11 MYH4 chr5: 34,749,464-34,757,802 q22 Loss 8,338 66.7 1.3 65.4 6.42E−14 9.66E−12 MYH4 chr5: 34,757,802-34,763,799 q22 Loss 5,997 63.6 1.3 62.3 4.46E−13 3.02E−11 MYH4 chr5: 34,763,799-34,888,925 q22 Loss 125,126 54.5 1.3 53.2 9.97E−11 1.85E−09 MYH4, MYH1, MYH2 chr5: 34,888,925-34,900,363 q22 Loss 11,438 57.6 1.3 56.3 1.75E−11 4.38E−10 chr5: 34,900,363-34,924,941 q22 Loss 24,578 60.6 1.3 59.3 2.88E−12 1.11E−10 MYH3 chr5: 34,924,941-34,997,381 q22 Loss 72,440 63.6 1.3 62.3 4.46E−13 3.02E−11 MYH3, SCO1, ADPRM, TMEM220 chr5: 34,997,381-35,219,397 q22 Loss 222,016 63.6 2.6 61.0 4.45E−12 1.48E−10 PIRT chr5: 35,219,397-35,273,015 q22 Loss 53,618 69.7 2.6 67.1 9.50E−14 1.12E−11 GPS2 chr5: 35,273,015-35,289,492 q22 Loss 16,477 72.7 2.6 70.1 1.22E−14 3.54E−12 chr5: 35,289,492-35,344,382 q22 Loss 54,890 78.8 3.9 74.8 1.33E−15 2.15E−12 chr5: 35,344,382-35,933,510 q22 Loss 589,128 78.8 5.3 73.5 9.14E−15 3.54E−12 SAT2, SHISA6, SHBG, DNAH9 chr5: 35,933,510-36,039,948 q22 Loss 106,438 75.8 3.9 71.8 1.21E−14 3.54E−12 DNAH9 chr5: 36,039,948-36,069,307 q22 Loss 29,359 63.6 1.3 62.3 4.46E−13 3.02E−11 DNAH9, ZNF18 chr5: 36,069,307-36,339,529 q22 Loss 270,222 60.6 1.3 59.3 2.88E−12 1.11E−10 ZNF18, LOC100685794, MAP2K4, LOC102155074, LOC102155028 chr5: 36,339,529-36,352,015 q22 Loss 12,486 57.6 1.3 56.3 1.75E−11 4.38E−10 LOC102155130 chr5: 36,352,015-36,361,706 q22 Loss 9,691 54.5 1.3 53.2 9.97E−11 1.85E−09 LOC102155130 chr5: 36,361,706-36,635,379 q22-q23 Loss 273,673 51.5 1.3 50.2 5.38E−10 8.10E−09 LOC102155130, LOC102155247 chr5: 36,635,379-36,706,216 q23 Loss 70,837 57.6 1.3 56.3 1.75E−11 4.38E−10 MYOCD chr5: 36,706,216-36,723,188 q23 Loss 16,972 63.6 1.3 62.3 4.46E−13 3.02E−11 MYOCD chr5: 36,723,188-36,935,181 q23 Loss 211,993 66.7 1.3 65.4 6.42E−14 9.66E−12 MYOCD, ARHGAP44 chr5: 36,935,181-36,991,263 q23 Loss 56,082 63.6 1.3 62.3 4.46E−13 3.02E−11 ARHGAP44, ELAC2, LOC102156372 chr5: 36,991,263-37,077,985 q23 Loss 86,722 54.5 1.3 53.2 9.97E−11 1.85E−09 LOC102156372 chr5: 37,077,985-37,345,754 q23 Loss 267,769 51.5 1.3 50.2 5.38E−10 8.10E−09 LOC102156372, LOC102155885, LOC102155946, LOC102156000 chr5: 37,345,754-37,378,710 q23 Loss 32,956 54.5 1.3 53.2 9.97E−11 1.85E−09 chr5: 37,378,710-37,400,359 q23 Loss 21,649 57.6 1.3 56.3 1.75E−11 4.38E−10 chr5: 37,400,359-37,418,572 q23 Loss 18,213 60.6 1.3 59.3 2.88E−12 1.11E−10 HS3ST3A1 chr5: 37,418,572-37,430,842 q23 Loss 12,270 63.6 1.3 62.3 4.46E−13 3.02E−11 HS3ST3A1 chr5: 37,430,842-37,475,789 q23 Loss 44,947 66.7 2.6 64.0 6.76E−13 3.91E−11 HS3ST3A1 chr5: 37,475,789-37,752,244 q23 Loss 276,455 69.7 2.6 67.1 9.50E−14 1.12E−11 HS3ST3A1, LOC608351, LOC102156188 chr5: 37,752,244-37,788,552 q23 Loss 36,308 72.7 2.6 70.1 1.22E−14 3.54E−12 chr5: 37,788,552-37,966,845 q23 Loss 178,293 75.8 2.6 73.1 1.43E−15 2.15E−12 COX10 chr5: 37,966,845-37,978,756 q23 Loss 11,911 72.7 2.6 70.1 1.22E−14 3.54E−12 COX10 chr5: 37,978,756-37,989,010 q23 Loss 10,254 72.7 1.3 71.4 1.05E−15 2.15E−12 COX10 chr5: 37,989,010-38,005,708 q23 Loss 16,698 69.7 1.3 68.4 8.56E−15 3.54E−12 chr5: 38,005,708-38,024,187 q23 Loss 18,479 66.7 1.3 65.4 6.42E−14 9.66E−12 HS3ST3B1 chr5: 38,024,187-38,065,129 q23 Loss 40,942 60.6 1.3 59.3 2.88E−12 1.11E−10 HS3ST3B1 chr5: 38,065,129-38,389,508 q23 Loss 324,379 57.6 1.3 56.3 1.75E−11 4.38E−10 HS3ST3B1 chr5: 38,389,508-38,483,690 q23 Loss 94,182 54.5 1.3 53.2 9.97E−11 1.85E−09 chr5: 38,483,690-38,679,152 q23 Loss 195,462 51.5 1.3 50.2 5.38E−10 8.10E−09 LOC102156520 chr5: 38,679,152-38,863,984 q23 Loss 184,832 54.5 1.3 53.2 9.97E−11 1.85E−09 LOC608772, PMP22 chr5: 38,863,984-39,121,648 q23 Loss 257,664 51.5 1.3 50.2 5.38E−10 8.10E−09 TEKT3, LOC102156927, CDRT4, TVP23B, FBXW10 chr5: 39,121,648-39,256,137 q23 Loss 134,489 54.5 1.3 53.2 9.97E−11 1.85E−09 FBXW10, LOC489518, LOC102151571, LOC608913, ZNF286A chr5: 39,256,137-39,810,518 q23 Loss 554,381 51.5 1.3 50.2 5.38E−10 8.10E−09 ZNF624, ZNF287, FAM211A, LOC100687347, TRPV2, UBB, CENPV, LOC100687735, PIGL, NCOR1 chr5: 39,810,518-39,860,472 q23 Loss 49,954 57.6 1.3 56.3 1.75E−11 4.38E−10 NCOR1, TTC19 chr5: 39,860,472-39,897,741 q23 Loss 37,269 54.5 1.3 53.2 9.97E−11 1.85E−09 TTC19, ZSWIM7, ADORA2B chr5: 39,897,741-39,943,405 q23 Loss 45,664 57.6 1.3 56.3 1.75E−11 4.38E−10 ADORA2B, SPECC1 chr5: 39,943,405-39,953,861 q23 Loss 10,456 63.6 1.3 62.3 4.46E−13 3.02E−11 SPECC1 chr5: 39,953,861-40,463,632 q23-q24 Loss 509,771 66.7 1.3 65.4 6.42E−14 9.66E−12 SPECC1, AKAP10, ULK2 chr5: 40,463,632-40,474,878 q24 Loss 11,246 63.6 1.3 62.3 4.46E−13 3.02E−11 ALDH3A1 chr5: 40,474,878-40,479,655 q24 Loss 4,777 60.6 1.3 59.3 2.88E−12 1.11E−10 chr5: 40,479,655-40,550,707 q24 Loss 71,052 57.6 1.3 56.3 1.75E−11 4.38E−10 SLC47A2, ALDH3A2 chr5: 40,550,707-40,588,700 q24 Loss 37,993 60.6 1.3 59.3 2.88E−12 1.11E−10 LOC100688308 chr5: 40,588,700-40,763,870 q24 Loss 175,170 63.6 1.3 62.3 4.46E−13 3.02E−11 LOC100688308, SLC47A1 chr5: 40,763,870-40,829,794 q24 Loss 65,924 60.6 1.3 59.3 2.88E−12 1.11E−10 RNF112, MFAP4 chr5: 40,829,794-41,116,952 q24 Loss 287,158 57.6 1.3 56.3 1.75E−11 4.38E−10 MFAP4, MAPK7, B9D1, EPN2, GRAP, SLC5A10, FAM83G chr5: 41,116,952-41,198,148 q24 Loss 81,196 54.5 1.3 53.2 9.97E−11 1.85E−09 PRPSAP2 chr5: 41,198,148-41,286,067 q24 Loss 87,919 57.6 1.3 56.3 1.75E−11 4.38E−10 SHMT1, SMCR8, TOP3A chr5: 41,286,067-41,401,440 q24 Loss 115,373 54.5 1.3 53.2 9.97E−11 1.85E−09 TOP3A, SMCR7, FLII, LLGL1, ALKBH5, MYO15A chr5: 41,401,440-41,429,338 q24 Loss 27,898 57.6 1.3 56.3 1.75E−11 4.38E−10 MYO15A, DRG2 chr5: 41,429,338-41,749,755 q24 Loss 320,417 60.6 1.3 59.3 2.88E−12 1.11E−10 DRG2, GID4, ATPAF2, LRRC48, TOM1L2, SREBF1, RAI1 chr5: 41,749,755-41,925,434 q24 Loss 175,679 63.6 1.3 62.3 4.46E−13 3.02E−11 RAI1, PEMT chr5: 41,925,434-41,961,024 q24 Loss 35,590 60.6 1.3 59.3 2.88E−12 1.11E−10 PEMT chr5: 41,961,024-42,181,266 q24 Loss 220,242 63.6 1.3 62.3 4.46E−13 3.02E−11 RASD1, MED9, NT5M, COPS3, FLCN chr5: 42,181,266-42,235,930 q24 Loss 54,664 66.7 1.3 65.4 6.42E−14 9.66E−12 FLCN, PLD6, LOC479530 chr5: 42,235,930-42,279,703 q24 Loss 43,773 63.6 1.3 62.3 4.46E−13 3.02E−11 LOC479530 chr5: 42,279,703-42,604,639 q24 Loss 324,936 60.6 1.3 59.3 2.88E−12 1.11E−10 LOC479530, LOC102153190, TNFRSF13B, USP22, DHRS7B chr5: 42,604,639-42,747,794 q24 Loss 143,155 54.5 1.3 53.2 9.97E−11 1.85E−09 DHRS7B, TMEM11, LOC102153833, C5H17orf103, LOC102154017, MAP2K3 chr5: 42,747,794-42,904,466 q24 Loss 156,672 57.6 1.3 56.3 1.75E−11 4.38E−10 MAP2K3, KCNJ12, LOC102157145 chr5: 42,904,466-42,966,420 q24 Loss 61,954 57.6 3.9 53.6 9.72E−10 1.34E−08 LOC102154136 chr5: 42,966,420-43,008,464 q24 Loss 42,044 63.6 3.9 59.7 3.09E−11 6.77E−10 chr5: 43,008,464-43,076,302 q24 Loss 67,838 60.6 3.9 56.7 1.79E−10 2.97E−09 LOC102154367 chr5: 43,076,302-43,189,705 q24 Loss 113,403 54.5 2.6 51.9 8.40E−10 1.18E−08 SERBP1 chr5: 43,189,705-43,361,590 q24 Loss 171,885 51.5 1.3 50.2 5.38E−10 8.10E−09 LOC102154658, IL12RB2, IL23R chr5: 43,361,590-43,470,080 q24 Loss 108,490 54.5 1.3 53.2 9.97E−11 1.85E−09 IL23R, LOC609111, C5H1orf141 chr5: 43,470,080-43,483,708 q24 Loss 13,628 51.5 1.3 50.2 5.38E−10 8.10E−09 LOC100685241 chr5: 44,768,774-45,069,038 q24 Loss 300,264 54.5 2.6 51.9 8.40E−10 1.18E−08 LEPR, LOC102156371, LOC609115, DNAJC6, AK4 chr5: 48,016,459-48,445,801 q24 Loss 429,342 54.5 2.6 51.9 8.40E−10 1.18E−08 INADL, LOC479551 chr5: 49,551,942-50,021,095 q24 Loss 469,153 54.5 2.6 51.9 8.40E−10 1.18E−08 C5H1orf87, CYP2J2, HOOK1 chr5: 50,047,157-50,091,376 q24 Loss 44,219 54.5 2.6 51.9 8.40E−10 1.18E−08 HOOK1 chr5: 50,091,376-50,114,330 q24 Loss 22,954 54.5 1.3 53.2 9.97E−11 1.85E−09 FGGY chr5: 50,114,330-50,436,698 q24 Loss 322,368 51.5 1.3 50.2 5.38E−10 8.10E−09 FGGY chr5: 50,436,698-50,448,468 q24 Loss 11,770 51.5 0.0 51.5 3.58E−11 7.73E−10 FGGY chr5: 51,048,547-51,059,816 q31 Loss 11,269 57.6 1.3 56.3 1.75E−11 4.38E−10 MYSM1 chr5: 51,059,816-51,252,744 q31 Loss 192,928 60.6 1.3 59.3 2.88E−12 1.11E−10 MYSM1, TACSTD2, OMA1 chr5: 51,252,744-51,563,921 q31 Loss 311,177 57.6 1.3 56.3 1.75E−11 4.38E−10 DAB1 chr5: 51,563,921-52,014,031 q31 Loss 450,110 54.5 1.3 53.2 9.97E−11 1.85E−09 LOC102156041, DAB1 chr5: 52,014,031-52,106,690 q31 Loss 92,659 57.6 1.3 56.3 1.75E−11 4.38E−10 DAB1 chr5: 52,106,690-52,139,816 q31 Loss 33,126 60.6 2.6 58.0 2.72E−11 6.21E−10 DAB1 chr5: 52,139,816-52,284,795 q31 Loss 144,979 63.6 2.6 61.0 4.45E−12 1.48E−10 DAB1 chr5: 52,284,795-52,533,879 q31 Loss 249,084 54.5 1.3 53.2 9.97E−11 1.85E−09 DAB1 chr5: 54,303,571-54,320,419 q31 Loss 16,848 51.5 1.3 50.2 5.38E−10 8.10E−09 DHCR24 chr5: 54,320,419-54,413,010 q31 Loss 92,591 54.5 1.3 53.2 9.97E−11 1.85E−09 DHCR24, C5H1orf177, TTC22, PARS2, TTC4 chr5: 54,413,010-54,610,336 q31 Loss 197,326 57.6 1.3 56.3 1.75E−11 4.38E−10 TTC4, HEATR8, FAM151A, ACOT11 chr5: 54,610,336-54,767,411 q31 Loss 157,075 60.6 2.6 58.0 2.72E−11 6.21E−10 LOC102151468, LOC102151517, SSBP3 chr5: 55,243,189-55,254,977 q31 Loss 11,788 63.6 2.6 61.0 4.45E−12 1.48E−10 GLIS1 chr5: 55,254,977-55,454,968 q31 Loss 199,991 63.6 3.9 59.7 3.09E−11 6.77E−10 GLIS1 chr5: 55,454,968-55,654,428 q31-q32 Loss 199,460 63.6 2.6 61.0 4.45E−12 1.48E−10 GLIS1, DMRTB1, C8A, LRP8 chr5: 55,654,428-55,724,923 q32 Loss 70,495 60.6 1.3 59.3 2.88E−12 1.11E−10 LRP8, MAGOH, C5H1orf123, CPT2 chr5: 55,724,923-55,840,582 q32 Loss 115,659 63.6 1.3 62.3 4.46E−13 3.02E−11 CPT2, LOC102153558, SLC1A7, PODN chr5: 55,840,582-55,916,484 q32 Loss 75,902 60.6 1.3 59.3 2.88E−12 1.11E−10 PODN, LOC102153929, SCP2 chr5: 55,916,484-55,992,635 q32 Loss 76,151 66.7 1.3 65.4 6.42E−14 9.66E−12 LOC102153929, SCP2, ECHDC2 chr5: 55,992,635-56,081,540 q32 Loss 88,905 63.6 1.3 62.3 4.46E−13 3.02E−11 ZYG11A chr5: 56,081,540-56,131,102 q32 Loss 49,562 60.6 1.3 59.3 2.88E−12 1.11E−10 SAMD11 chr5: 56,533,402-56,539,243 q32 Loss 5,841 51.5 1.3 50.2 5.38E−10 8.10E−09 chr5: 56,539,243-56,713,902 q32 Loss 174,659 54.5 1.3 53.2 9.97E−11 1.85E−09 AURKAIP1, CCNL2, MRPL20, ANKRD65, TMEM88B, VWA1, ATAD3B, TMEM240, SSU72, C5H1orf233, MIB2, MMP23B, CDK11B chr5: 56,713,902-56,760,282 q32 Loss 46,380 51.5 1.3 50.2 5.38E−10 8.10E−09 CDK11B, SLC35E2B, NADK chr5: 56,800,229-56,812,468 q32 Loss 12,239 54.5 1.3 53.2 9.97E−11 1.85E−09 GNB1 chr5: 56,812,468-56,835,624 q32 Loss 23,156 57.6 1.3 56.3 1.75E−11 4.38E−10 GNB1 chr5: 56,835,624-56,859,924 q32 Loss 24,300 54.5 1.3 53.2 9.97E−11 1.85E−09 GNB1 chr5: 56,879,215-56,892,317 q32 Loss 13,102 51.5 1.3 50.2 5.38E−10 8.10E−09 GNB1 chr5: 57,333,494-57,373,774 q32 Loss 40,280 54.5 3.9 50.6 4.94E−09 5.94E−08 PLCH2, PANK4 chr5: 57,373,774-57,397,914 q32 Loss 24,140 57.6 3.9 53.6 9.72E−10 1.34E−08 PANK4, HES5, LOC102155829 chr5: 57,397,914-57,427,153 q32 Loss 29,239 60.6 3.9 56.7 1.79E−10 2.97E−09 LOC102155829, TNFRSF14 chr5: 57,427,153-57,441,148 q32 Loss 13,995 60.6 6.6 54.0 3.94E−09 4.98E−08 FAM213B, MMEL1 chr5: 57,441,148-57,473,452 q32 Loss 32,304 63.6 6.6 57.1 7.51E−10 1.12E−08 MMEL1, LOC479559 chr5: 57,473,452-57,551,025 q32 Loss 77,573 63.6 7.9 55.7 2.91E−09 3.74E−08 LOC479559, TTC34 chr5: 57,551,025-57,607,976 q32 Loss 56,951 63.6 9.2 54.4 1.00E−08 1.18E−07 chr5: 57,607,976-57,621,918 q32 Loss 13,942 63.6 10.5 53.1 3.13E−08 3.41E−07 chr5: 57,621,918-57,686,792 q32 Loss 64,874 69.7 10.5 59.2 1.18E−09 1.59E−08 ACTRT2 chr5: 57,686,792-57,792,964 q32 Loss 106,172 69.7 15.8 53.9 7.35E−08 7.77E−07 LOC102156624 chr5: 57,792,964-57,810,561 q32 Loss 17,597 69.7 18.4 51.3 4.48E−07 4.42E−06 LOC102156763, PRDM16 chr5: 57,835,337-57,844,842 q32 Loss 9,505 72.7 19.7 53.0 2.14E−07 2.19E−06 PRDM16 chr5: 57,844,842-57,946,330 q32 Loss 101,488 72.7 22.4 50.4 1.06E−06 1.04E−05 PRDM16 chr5: 58,119,121-58,237,076 q32 Loss 117,955 63.6 13.2 50.5 2.37E−07 2.43E−06 MEGF6, TPRG1L, WRAP73, TP73 chr5: 58,237,076-58,255,637 q32 Loss 18,561 63.6 3.9 59.7 3.09E−11 6.77E−10 TP73 chr5: 58,255,637-58,708,022 q32 Loss 452,385 60.6 3.9 56.7 1.79E−10 2.97E−09 CCDC27, SMIM1, LRRC47, CEP104, DFFB, C5H1orf174, LOC102151771 chr5: 58,708,022-58,727,983 q32 Loss 19,961 66.7 5.3 61.4 2.81E−11 6.38E−10 LOC102151822, LOC102151862 chr5: 58,727,983-58,911,333 q32 Loss 183,350 69.7 9.2 60.5 3.47E−10 5.61E−09 AJAP1 chr5: 58,911,333-59,057,612 q32 Loss 146,279 69.7 5.3 64.4 4.36E−12 1.48E−10 AJAP1 chr5: 59,057,612-59,113,878 q32 Loss 56,266 66.7 3.9 62.7 4.94E−12 1.59E−10 chr5: 59,113,878-59,187,182 q32 Loss 73,304 63.6 3.9 59.7 3.09E−11 6.77E−10 chr5: 59,187,182-59,326,374 q32 Loss 139,192 60.6 3.9 56.7 1.79E−10 2.97E−09 ISG15 chr5: 59,326,374-59,781,603 q32 Loss 455,229 63.6 3.9 59.7 3.09E−11 6.77E−10 PUSL1, CPSF3L, GLTPD1, DVL1 chr5: 59,781,603-59,794,743 q32 Loss 13,140 63.6 5.3 58.4 1.67E−10 2.84E−09 chr5: 59,794,743-59,805,785 q32 Loss 11,042 63.6 6.6 57.1 7.51E−10 1.12E−08 chr5: 59,805,785-59,836,239 q32 Loss 30,454 57.6 6.6 51.0 1.92E−08 2.15E−07 NPHP4 chr5: 60,170,663-60,180,879 q32 Loss 10,216 54.5 3.9 50.6 4.94E−09 5.94E−08 ACOT7 chr5: 60,180,879-60,354,077 q32 Loss 173,198 57.6 3.9 53.6 9.72E−10 1.34E−08 ACOT7, HES2, PEX10, ESPN, TNFRSF25, PLEKHG5 chr5: 60,354,077-60,427,986 q32 Loss 73,909 69.7 3.9 65.7 7.29E−13 4.00E−11 NOL9, TAS1R1, ZBTB48, KLHL21 chr5: 60,427,986-60,508,241 q32 Loss 80,255 69.7 1.3 68.4 8.56E−15 3.54E−12 PHF13, THAP3, DNAJC11 chr5: 60,508,241-60,541,238 q32 Loss 32,997 66.7 1.3 65.4 6.42E−14 9.66E−12 DNAJC11, LOC102154443 chr5: 60,541,238-60,684,920 q32 Loss 143,682 63.6 1.3 62.3 4.46E−13 3.02E−11 LOC102154443, CAMTA1 chr5: 60,684,920-60,852,161 q32 Loss 167,241 69.7 2.6 67.1 9.50E−14 1.12E−11 CAMTA1 chr5: 60,852,161-61,036,261 q32 Loss 184,100 66.7 2.6 64.0 6.76E−13 3.91E−11 CAMTA1 chr5: 61,036,261-61,044,908 q32 Loss 8,647 66.7 3.9 62.7 4.94E−12 1.59E−10 CAMTA1 chr5: 61,044,908-61,141,116 q32-q33 Loss 96,208 72.7 6.6 66.1 3.20E−12 1.22E−10 CAMTA1 chr5: 61,141,116-61,316,581 q33 Loss 175,465 75.8 11.8 63.9 1.05E−10 1.93E−09 CAMTA1, LOC607748 chr5: 61,316,581-61,329,461 q33 Loss 12,880 66.7 6.6 60.1 1.33E−10 2.43E−09 CAMTA1 chr5: 61,329,461-61,367,490 q33 Loss 38,029 63.6 6.6 57.1 7.51E−10 1.12E−08 CAMTA1 chr5: 61,367,490-61,378,222 q33 Loss 10,732 57.6 6.6 51.0 1.92E−08 2.15E−07 CAMTA1 chr5: 61,378,222-61,394,755 q33 Loss 16,533 54.5 3.9 50.6 4.94E−09 5.94E−08 CAMTA1 chr5: 61,394,755-61,402,826 q33 Loss 8,071 54.5 1.3 53.2 9.97E−11 1.85E−09 CAMTA1 chr5: 61,812,024-61,901,869 q33 Loss 89,845 51.5 1.3 50.2 5.38E−10 8.10E−09 SLC45A1, RERE chr5: 62,305,554-62,381,943 q33 Loss 76,389 51.5 1.3 50.2 5.38E−10 8.10E−09 ENO1, CA6 chr5: 62,381,943-62,555,678 q33 Loss 173,735 54.5 1.3 53.2 9.97E−11 1.85E−09 SLC2A5, GPR157, H6PD chr5: 62,555,678-62,785,659 q33 Loss 229,981 57.6 1.3 56.3 1.75E−11 4.38E−10 H6PD, SPSB1 chr5: 62,785,659-63,063,012 q33 Loss 277,353 54.5 1.3 53.2 9.97E−11 1.85E−09 SLC25A33, TMEM201, PIK3CD, CLSTN1, CTNNB1P1 chr5: 63,063,012-63,103,582 q33 Loss 40,570 57.6 1.3 56.3 1.75E−11 4.38E−10 CTNNB1P1 chr5: 63,103,582-63,125,693 q33 Loss 22,111 60.6 1.3 59.3 2.88E−12 1.11E−10 LZIC, NMNAT1 chr5: 63,125,693-63,258,956 q33 Loss 133,263 66.7 1.3 65.4 6.42E−14 9.66E−12 NMNAT1, RBP7, UBE4B chr5: 63,258,956-63,349,218 q33 Loss 90,262 63.6 1.3 62.3 4.46E−13 3.02E−11 UBE4B, LOC102153284 chr5: 63,349,218-63,514,128 q33 Loss 164,910 57.6 1.3 56.3 1.75E−11 4.38E−10 KIF1B chr5: 63,514,128-63,617,392 q33 Loss 103,264 63.6 1.3 62.3 4.46E−13 3.02E−11 KIF1B, LOC100682843, LOC489647, GAS8, DBNDD1 chr5: 63,617,392-63,679,390 q33 Loss 61,998 60.6 1.3 59.3 2.88E−12 1.11E−10 DBNDD1, LOC102155075, CENPBD1, DEF8 chr5: 63,679,390-63,712,820 q33 Loss 33,430 57.6 1.3 56.3 1.75E−11 4.38E−10 TUBB3, MC1R, TCF25 chr5: 63,712,820-63,882,807 q33 Loss 169,987 54.5 1.3 53.2 9.97E−11 1.85E−09 TCF25, SPIRE2, FANCA, ZNF276, VPS9D1, SPATA2L, CDK10 chr5: 63,882,807-64,185,061 q33 Loss 302,254 57.6 1.3 56.3 1.75E−11 4.38E−10 CDK10, SPATA33, CHMP1A, DPEP1, LOC102155777, CPNE7, RPL13, SPG7, ANKRD11 chr5: 64,185,061-64,198,235 q33 Loss 13,174 57.6 2.6 54.9 1.56E−10 2.67E−09 ANKRD11 chr5: 64,198,235-64,262,537 q33 Loss 64,302 57.6 3.9 53.6 9.72E−10 1.34E−08 ANKRD11, SLC22A31, CDH15 chr5: 64,262,537-64,399,872 q33 Loss 137,335 60.6 3.9 56.7 1.79E−10 2.97E−09 CDH15, ACSF3, CAMTA1 chr5: 64,399,872-64,419,690 q33 Loss 19,818 57.6 3.9 53.6 9.72E−10 1.34E−08 CAMTA1 chr5: 64,712,902-64,810,771 q33 Loss 97,869 60.6 6.6 54.0 3.94E−09 4.98E−08 ZC3H18 chr5: 64,810,771-64,855,695 q33 Loss 44,924 60.6 5.3 55.3 9.21E−10 1.29E−08 ZFPM1 chr5: 64,855,695-64,939,536 q33 Loss 83,841 63.6 6.6 57.1 7.51E−10 1.12E−08 ZFPM1, ZNF469 chr5: 64,939,536-65,041,471 q33 Loss 101,935 66.7 7.9 58.8 5.39E−10 8.10E−09 chr5: 65,041,471-65,142,070 q33 Loss 100,599 66.7 6.6 60.1 1.33E−10 2.43E−09 chr5: 65,142,070-65,190,048 q33 Loss 47,978 63.6 2.6 61.0 4.45E−12 1.48E−10 BANP chr5: 65,190,048-65,203,750 q33 Loss 13,702 60.6 2.6 58.0 2.72E−11 6.21E−10 BANP chr5: 65,203,750-65,420,725 q33 Loss 216,975 57.6 2.6 54.9 1.56E−10 2.67E−09 BANP, CA5A, SLC7A5, KLHDC4 chr5: 65,420,725-65,513,651 q33 Loss 92,926 60.6 2.6 58.0 2.72E−11 6.21E−10 KLHDC4, JPH3 chr5: 65,513,651-65,596,667 q33 Loss 83,016 60.6 1.3 59.3 2.88E−12 1.11E−10 JPH3 chr5: 65,596,667-65,928,870 q33 Loss 332,203 63.6 1.3 62.3 4.46E−13 3.02E−11 LOC102152720, ZCCHC14, MAP1LC3B, FBXO31, LOC609239, LOC102153014 chr5: 65,928,870-66,430,556 q33 Loss 501,686 63.6 2.6 61.0 4.45E−12 1.48E−10 LOC102153014, LZIC, NMNAT1, LOC102153168, LOC102153219, FOXL1, FOXC2, LOC102153392, MTHFSD, FOXF1, LOC102153633 chr5: 66,430,556-66,532,101 q33 Loss 101,545 60.6 2.6 58.0 2.72E−11 6.21E−10 chr5: 66,532,101-66,660,467 q33 Loss 128,366 57.6 2.6 54.9 1.56E−10 2.67E−09 chr5: 66,660,467-66,940,063 q33 Loss 279,596 57.6 1.3 56.3 1.75E−11 4.38E−10 TUBB3, SPIRE2, IRF8, LOC489654, COX4I1, EMC8 chr5: 66,940,063-66,978,728 q33 Loss 38,665 54.5 1.3 53.2 9.97E−11 1.85E−09 C5H16orf74 chr5: 66,978,728-67,024,605 q33 Loss 45,877 60.6 6.6 54.0 3.94E−09 4.98E−08 GINS2, KIAA0182 chr5: 67,024,605-67,107,364 q33 Loss 82,759 60.6 7.9 52.7 1.46E−08 1.64E−07 KIAA0182 chr5: 67,107,364-67,134,097 q33 Loss 26,733 60.6 5.3 55.3 9.21E−10 1.29E−08 KIAA0182 chr5: 67,134,097-67,358,286 q33 Loss 224,189 54.5 3.9 50.6 4.94E−09 5.94E−08 KIAA0182 chr5: 67,358,286-67,376,754 q33 Loss 18,468 54.5 2.6 51.9 8.40E−10 1.18E−08 chr5: 67,464,481-67,505,212 q33 Loss 40,731 57.6 2.6 54.9 1.56E−10 2.67E−09 FAM92B, KIAA0513 chr5: 67,505,212-67,694,200 q33 Loss 188,988 60.6 2.6 58.0 2.72E−11 6.21E−10 KIAA0513, ZDHHC7, CRISPLD2 chr5: 67,694,200-68,175,856 q33 Loss 481,656 57.6 1.3 56.3 1.75E−11 4.38E−10 CRISPLD2, USP10, KLHL36, COTL1, LOC102152359, TLDC1, ATP2C2, LOC102152587, WFDC1, KCNG4, ADAD2, TAF1C chr5: 68,175,856-68,238,327 q33 Loss 62,471 60.6 1.3 59.3 2.88E−12 1.11E−10 TAF1C, DNAAF1, HSDL1, MBTPS1 chr5: 68,238,327-68,254,252 q33 Loss 15,925 63.6 1.3 62.3 4.46E−13 3.02E−11 MBTPS1 chr5: 68,254,252-68,321,004 q33 Loss 66,752 69.7 6.6 63.1 2.16E−11 5.35E−10 MBTPS1, SLC38A8, NECAB2 chr5: 68,321,004-68,330,658 q33 Loss 9,654 66.7 3.9 62.7 4.94E−12 1.59E−10 NECAB2 chr5: 68,330,658-68,354,363 q33 Loss 23,705 57.6 3.9 53.6 9.72E−10 1.34E−08 NECAB2, OSGIN1 chr5: 68,354,363-68,452,845 q33 Loss 98,482 57.6 1.3 56.3 1.75E−11 4.38E−10 OSGIN1, MLYCD chr5: 68,452,845-68,492,076 q33 Loss 39,231 66.7 1.3 65.4 6.42E−14 9.66E−12 HSBP1 chr5: 68,492,076-68,596,350 q33 Loss 104,274 69.7 1.3 68.4 8.56E−15 3.54E−12 CDH13 chr5: 68,596,350-68,755,793 q33 Loss 159,443 60.6 1.3 59.3 2.88E−12 1.11E−10 CDH13 chr5: 68,755,793-68,929,916 q33 Loss 174,123 57.6 1.3 56.3 1.75E−11 4.38E−10 CDH13 chr5: 68,929,916-69,299,668 q33 Loss 369,752 54.5 1.3 53.2 9.97E−11 1.85E−09 CDH13, LOC102153763 chr5: 69,299,668-69,397,643 q33-q34 Loss 97,975 57.6 1.3 56.3 1.75E−11 4.38E−10 CDH13 chr5: 69,397,643-69,460,633 q34 Loss 62,990 60.6 1.3 59.3 2.88E−12 1.11E−10 CDH13 chr5: 69,460,633-69,727,892 q34 Loss 267,259 63.6 1.3 62.3 4.46E−13 3.02E−11 CDH13 chr5: 69,727,892-69,811,895 q34 Loss 84,003 60.6 1.3 59.3 2.88E−12 1.11E−10 LOC102153888 chr5: 69,811,895-69,912,645 q34 Loss 100,750 63.6 1.3 62.3 4.46E−13 3.02E−11 LOC102153888, LOC100686562, LOC102154063, MPHOSPH6 chr5: 69,912,645-69,976,616 q34 Loss 63,971 66.7 1.3 65.4 6.42E−14 9.66E−12 HSD17B2 chr5: 69,976,616-70,048,532 q34 Loss 71,916 63.6 1.3 62.3 4.46E−13 3.02E−11 HSD17B2, SDR42E1 chr5: 70,048,532-70,118,809 q34 Loss 70,277 60.6 1.3 59.3 2.88E−12 1.11E−10 PLCG2 chr5: 70,118,809-70,256,802 q34 Loss 137,993 51.5 1.3 50.2 5.38E−10 8.10E−09 PLCG2, LOC102154738, CMIP chr5: 70,256,802-70,325,943 q34 Loss 69,141 57.6 1.3 56.3 1.75E−11 4.38E−10 CMIP chr5: 70,325,943-70,419,585 q34 Loss 93,642 54.5 1.3 53.2 9.97E−11 1.85E−09 CMIP chr5: 70,419,585-70,516,517 q34 Loss 96,932 51.5 1.3 50.2 5.38E−10 8.10E−09 LOC102154784 chr5: 70,561,384-71,093,340 q34 Loss 531,956 51.5 1.3 50.2 5.38E−10 8.10E−09 GAN, LOC102154971, BCMO1, PKD1L2, GCSH, C5H16orf46, ATMIN, CENPN, CMC2, CDYL2 chr5: 71,093,340-71,434,076 q34 Loss 340,736 54.5 1.3 53.2 9.97E−11 1.85E−09 CDYL2, DYNLRB2, LOC102156069 chr5: 71,434,076-71,693,390 q34 Loss 259,314 51.5 1.3 50.2 5.38E−10 8.10E−09 LOC102156069, LOC102156020, HSBP1 chr5: 72,431,034-72,750,582 q34 Loss 319,548 54.5 1.3 53.2 9.97E−11 1.85E−09 WWOX chr5: 72,750,582-72,808,838 q34 Loss 58,256 51.5 1.3 50.2 5.38E−10 8.10E−09 WWOX chr5: 73,342,822-73,464,745 q34 Loss 121,923 54.5 1.3 53.2 9.97E−11 1.85E−09 VAT1L chr5: 73,464,745-73,541,058 q34 Loss 76,313 57.6 1.3 56.3 1.75E−11 4.38E−10 VAT1L, NUDT7 chr5: 73,541,058-73,686,949 q34 Loss 145,891 54.5 1.3 53.2 9.97E−11 1.85E−09 chr5: 73,686,949-73,843,644 q34 Loss 156,695 57.6 1.3 56.3 1.75E−11 4.38E−10 ADAMTS18 chr5: 73,843,644-73,896,791 q34 Loss 53,147 54.5 1.3 53.2 9.97E−11 1.85E−09 ADAMTS18 chr5: 74,982,757-75,025,354 q34 Loss 42,597 54.5 1.3 53.2 9.97E−11 1.85E−09 chr5: 75,025,354-75,234,343 q34 Loss 208,989 57.6 1.3 56.3 1.75E−11 4.38E−10 TERF2IP, KARS, ADAT1, GABARAPL2 chr5: 75,234,343-75,248,653 q34 Loss 14,310 54.5 1.3 53.2 9.97E−11 1.85E−09 ADAT1, GABARAPL2, TMEM231 chr5: 75,248,653-75,398,797 q34 Loss 150,144 51.5 1.3 50.2 5.38E−10 8.10E−09 TMEM231, CHST6, TMEM170A, CFDP1 chr5: 75,398,797-75,540,382 q34 Loss 141,585 57.6 1.3 56.3 1.75E−11 4.38E−10 CFDP1, BCAR1, LOC610373, LOC479649, CTRB2 chr5: 75,540,382-75,641,073 q34 Loss 100,691 51.5 1.3 50.2 5.38E−10 8.10E−09 ZFP1, LDHD, ZNRF1 chr5: 75,641,073-75,717,063 q34 Loss 75,990 54.5 1.3 53.2 9.97E−11 1.85E−09 ZNRF1, WDR59 chr5: 75,717,063-76,023,587 q34 Loss 306,524 63.6 1.3 62.3 4.46E−13 3.02E−11 WDR59, FA2H, RFWD3, GLG1 chr5: 76,023,587-76,053,502 q34 Loss 29,915 60.6 1.3 59.3 2.88E−12 1.11E−10 GLG1 chr5: 76,053,502-76,096,004 q34 Loss 42,502 57.6 1.3 56.3 1.75E−11 4.38E−10 GLG1 chr5: 76,096,004-76,152,809 q34 Loss 56,805 54.5 1.3 53.2 9.97E−11 1.85E−09 GLG1 chr5: 76,152,809-76,280,694 q34 Loss 127,885 57.6 1.3 56.3 1.75E−11 4.38E−10 GLG1, LOC100688256, PDPR, CLEC18A, LOC610465 chr5: 76,280,694-76,401,056 q34 Loss 120,362 54.5 1.3 53.2 9.97E−11 1.85E−09 LOC610465, EXOSC6, AARS, DDX19B, LOC102154605, DDX19A, ST3GAL2 chr5: 76,401,056-76,426,024 q34 Loss 24,968 60.6 1.3 59.3 2.88E−12 1.11E−10 ST3GAL2 chr5: 76,426,024-76,480,400 q34 Loss 54,376 63.6 1.3 62.3 4.46E−13 3.02E−11 ST3GAL2, FUK, COG4 chr5: 76,480,400-76,509,943 q34 Loss 29,543 66.7 1.3 65.4 6.42E−14 9.66E−12 COG4, SF3B3 chr5: 76,509,943-76,722,201 q34 Loss 212,258 69.7 1.3 68.4 8.56E−15 3.54E−12 SF3B3, IL34, MTSS1L, VAC14 chr5: 76,722,201-76,779,204 q34 Loss 57,003 66.7 1.3 65.4 6.42E−14 9.66E−12 VAC14, HYDIN chr5: 76,779,204-76,903,015 q34 Loss 123,811 63.6 1.3 62.3 4.46E−13 3.02E−11 HYDIN chr5: 76,903,015-77,112,947 q34 Loss 209,932 57.6 1.3 56.3 1.75E−11 4.38E−10 HYDIN, LOC102156275 chr5: 77,112,947-77,205,471 q34 Loss 92,524 60.6 1.3 59.3 2.88E−12 1.11E−10 CMTR2, LOC102156521, CALB2 chr5: 77,205,471-77,241,951 q34 Loss 36,480 57.6 1.3 56.3 1.75E−11 4.38E−10 LOC479662 chr5: 77,241,951-77,306,877 q34 Loss 64,926 51.5 1.3 50.2 5.38E−10 8.10E−09 LOC479662, ZNF23, ZNF19 chr5: 77,520,487-77,574,687 q34 Loss 54,200 51.5 1.3 50.2 5.38E−10 8.10E−09 AP1G1 chr5: 77,574,687-77,619,106 q34-q35 Loss 44,419 54.5 1.3 53.2 9.97E−11 1.85E−09 AP1G1, ATXN1L chr5: 77,619,106-77,799,802 q35 Loss 180,696 57.6 1.3 56.3 1.75E−11 4.38E−10 ATXN1L, ZNF821, IST1, PKD1L3, DHODH, LOC612963, LOC479668 chr5: 77,799,802-77,820,747 q35 Loss 20,945 54.5 1.3 53.2 9.97E−11 1.85E−09 LOC612963, LOC479668, DHX38 chr5: 77,820,747-78,019,912 q35 Loss 199,165 51.5 1.3 50.2 5.38E−10 8.10E−09 DHX38, PMFBP1 chr5: 78,019,912-78,134,883 q35 Loss 114,971 54.5 1.3 53.2 9.97E−11 1.85E−09 chr5: 78,459,732-78,473,806 q35 Loss 14,074 63.6 1.3 62.3 4.46E−13 3.02E−11 ZFHX3 chr5: 78,473,806-78,600,625 q35 Loss 126,819 66.7 1.3 65.4 6.42E−14 9.66E−12 ZFHX3 chr5: 78,600,625-78,710,020 q35 Loss 109,395 63.6 1.3 62.3 4.46E−13 3.02E−11 ZFHX3 chr5: 78,710,020-78,909,765 q35 Loss 199,745 57.6 1.3 56.3 1.75E−11 4.38E−10 ZFHX3, LOC102153471 chr5: 78,909,765-79,145,756 q35 Loss 235,991 54.5 1.3 53.2 9.97E−11 1.85E−09 LOC102153471 chr5: 79,145,756-79,179,389 q35 Loss 33,633 51.5 1.3 50.2 5.38E−10 8.10E−09 chr5: 80,595,921-80,616,230 q35 Loss 20,309 51.5 0.0 51.5 3.58E−11 7.73E−10 TANGO6 chr5: 80,616,230-80,765,577 q35 Loss 149,347 51.5 1.3 50.2 5.38E−10 8.10E−09 TANGO6, CDH1 chr5: 80,765,577-80,799,104 q35 Loss 33,527 54.5 1.3 53.2 9.97E−11 1.85E−09 CDH1 chr5: 80,799,104-80,804,686 q35 Loss 5,582 57.6 1.3 56.3 1.75E−11 4.38E−10 CDH1 chr5: 80,804,686-80,914,064 q35 Loss 109,378 66.7 1.3 65.4 6.42E−14 9.66E−12 CDH1, CDH3, ZFP90 chr5: 80,914,064-81,187,351 q35 Loss 273,287 63.6 1.3 62.3 4.46E−13 3.02E−11 ZFP90, SMPD3, PRMT7 chr5: 81,187,351-81,255,412 q35 Loss 68,061 57.6 1.3 56.3 1.75E−11 4.38E−10 PRMT7, SLC7A6OS, SLC7A6 chr5: 81,569,146-81,577,803 q35 Loss 8,657 51.5 1.3 50.2 5.38E−10 8.10E−09 PSKH1 chr5: 81,577,803-81,609,089 q35 Loss 31,286 54.5 1.3 53.2 9.97E−11 1.85E−09 PSKH1, NRN1L, EDC4 chr5: 81,609,089-81,680,133 q35 Loss 71,044 57.6 1.3 56.3 1.75E−11 4.38E−10 EDC4, NUTF2, THAP11, CENPT, TSNAXIP1, RANBP10 chr5: 81,680,133-81,863,442 q35 Loss 183,309 60.6 1.3 59.3 2.88E−12 1.11E−10 RANBP10, GFOD2, C5H16orf86, ENKD1, PARD6A, ACD, RLTPR, CTCF chr5: 81,863,442-82,011,077 q35 Loss 147,635 57.6 1.3 56.3 1.75E−11 4.38E−10 CTCF, FAM65A, AGRP, ATP6V0D1, HSD11B2, ZDHHC1, TPPP3, LRRC36 chr5: 82,011,077-82,282,553 q35 Loss 271,476 54.5 1.3 53.2 9.97E−11 1.85E−09 TPPP3, LRRC36, KCTD19, PLEKHG4, SLC9A5, FHOD1, TMEM208, LRRC29, LOC102155969, ELMO3, E2F4, EXOC3L1, KIAA0895L, NOL3, HSF4, FBXL8, TRADD, B3GNT9, C5H16orf70, CBFB chr5: 82,282,553-82,519,611 q35 Loss 237,058 57.6 1.3 56.3 1.75E−11 4.38E−10 CBFB, CES2, FAM96B, RRAD, CDH16, PDP2, CA7, NAE1, CCDC79 chr5: 82,519,611-82,616,288 q35 Loss 96,677 54.5 1.3 53.2 9.97E−11 1.85E−09 CCDC79, DYNC1LI2 chr5: 82,616,288-82,766,028 q35 Loss 149,740 57.6 1.3 56.3 1.75E−11 4.38E−10 CMTM4, CMTM3, LOC479694, CMTM1, CKLF, TK2 chr5: 82,766,028-82,782,187 q35 Loss 16,159 57.6 5.3 52.3 4.74E−09 5.74E−08 BEAN1 chr5: 82,782,187-82,798,102 q35 Loss 15,915 60.6 5.3 55.3 9.21E−10 1.29E−08 BEAN1 chr5: 82,798,102-82,836,281 q35 Loss 38,179 66.7 9.2 57.5 1.95E−09 2.61E−08 BEAN1, CDH5 chr5: 82,836,281-82,845,474 q35 Loss 9,193 69.7 9.2 60.5 3.47E−10 5.61E−09 CDH5 chr5: 82,845,474-83,012,607 q35 Loss 167,133 72.7 9.2 63.5 5.64E−11 1.21E−09 CDH5 chr5: 83,012,607-83,027,347 q35 Loss 14,740 69.7 9.2 60.5 3.47E−10 5.61E−09 chr5: 83,027,347-83,075,902 q35 Loss 48,555 63.6 3.9 59.7 3.09E−11 6.77E−10 chr5: 83,075,902-83,178,724 q35 Loss 102,822 63.6 2.6 61.0 4.45E−12 1.48E−10 chr5: 83,178,724-83,387,764 q35 Loss 209,040 60.6 1.3 59.3 2.88E−12 1.11E−10 chr5: 83,387,764-83,589,799 q35 Loss 202,035 63.6 1.3 62.3 4.46E−13 3.02E−11 chr5: 83,589,799-83,599,745 q35 Loss 9,946 60.6 1.3 59.3 2.88E−12 1.11E−10 chr5: 83,599,745-83,708,089 q35 Loss 108,344 54.5 1.3 53.2 9.97E−11 1.85E−09 chr5: 83,708,089-84,025,109 q35-q36 Loss 317,020 51.5 1.3 50.2 5.38E−10 8.10E−09 LOC102152798, CDH11 chr5: 84,849,361-85,134,032 q36 Loss 284,671 51.5 1.3 50.2 5.38E−10 8.10E−09 chr5: 85,324,982-85,369,946 q36 Loss 44,964 54.5 2.6 51.9 8.40E−10 1.18E−08 chr5: 86,316,203-86,469,234 q36 Loss 153,031 54.5 2.6 51.9 8.40E−10 1.18E−08 LOC102153075, CDH8 chr5: 87,498,411-87,778,294 q36 Loss 279,883 54.5 2.6 51.9 8.40E−10 1.18E−08 chr5: 87,778,294-87,872,957 q36 Loss 94,663 60.6 2.6 58.0 2.72E−11 6.21E−10 LOC479698 chr5: 87,872,957-88,011,870 q36 Loss 138,913 63.6 2.6 61.0 4.45E−12 1.48E−10 LOC479698 chr5: 88,011,870-88,125,949 q36 Loss 114,079 60.6 2.6 58.0 2.72E−11 6.21E−10 chr5: 88,125,949-88,139,142 q36 Loss 13,193 57.6 2.6 54.9 1.56E−10 2.67E−09 chr5: 88,139,142-88,428,223 q36 Loss 289,081 54.5 2.6 51.9 8.40E−10 1.18E−08 chr5: 88,428,223-88,692,989 q36 Loss 264,766 57.6 2.6 54.9 1.56E−10 2.67E−09 chr5: 88,692,989-88,915,250 q36 Loss 222,261 63.6 5.3 58.4 1.67E−10 2.84E−09 chr31: 3,574,978-3,606,485 q11 Gain 31,507 54.5 3.9 50.6 4.94E−09 4.90E−07 chr31: 3,606,485-3,713,760 q11 Gain 107,275 57.6 3.9 53.6 9.72E−10 2.43E−07 chr31: 3,713,760-3,927,488 q11 Gain 213,728 57.6 5.3 52.3 4.74E−09 4.90E−07 chr31: 4,494,597-6,181,347 q12 Gain 1,686,750 57.6 5.3 52.3 4.74E−09 4.90E−07 LOC102152860, RPL6P, LOC478380 chr31: 6,742,546-8,389,449 q12 Gain 1,646,903 57.6 5.3 52.3 4.74E−09 4.90E−07 ROBO1, LOC102153268, LOC102153225 chr31: 8,389,449-8,517,658 q12 Gain 128,209 57.6 6.6 51.0 1.92E−08 1.26E−06 ROBO1 chr31: 8,517,658-8,645,671 q12 Gain 128,013 60.6 6.6 54.0 3.94E−09 4.90E−07 ROBO1 chr31: 8,645,671-8,751,964 q12 Gain 106,293 60.6 5.3 55.3 9.21E−10 2.43E−07 chr31: 8,751,964-9,390,589 q12 Gain 638,625 57.6 5.3 52.3 4.74E−09 4.90E−07 chr31: 9,390,589-9,821,887 q12 Gain 431,298 60.6 5.3 55.3 9.21E−10 2.43E−07 LOC487691 chr31: 9,821,887-9,909,162 q12 Gain 87,275 63.6 5.3 58.4 1.67E−10 2.43E−07 LOC487691 chr31: 9,909,162-9,996,577 q12 Gain 87,415 60.6 5.3 55.3 9.21E−10 2.43E−07 LOC478381, LOC487691 chr31: 9,996,577-10,721,940 q12 Gain 725,363 57.6 5.3 52.3 4.74E−09 4.90E−07 LOC487691, LOC102153311 chr31: 10,721,940-10,990,663 q12 Gain 268,723 60.6 5.3 55.3 9.21E−10 2.43E−07 LOC102153814 chr31: 10,990,663-11,662,746 q12-q13 Gain 672,083 60.6 6.6 54.0 3.94E−09 4.90E−07 LIPI, RBM11, LOC102154069, LOC478384, LOC102153846, HSPA13, LOC102154431, SAMSN1 chr31: 11,662,746-11,814,984 q13 Gain 152,238 60.6 5.3 55.3 9.21E−10 2.43E−07 SAMSN1, LOC100684485 chr31: 11,814,984-12,217,908 q13 Gain 402,924 63.6 6.6 57.1 7.51E−10 2.43E−07 NRIP1 chr31: 12,217,908-12,526,736 q13 Gain 308,828 60.6 6.6 54.0 3.94E−09 4.90E−07 LOC102155616, LOC102154762 chr31: 12,526,736-12,876,407 q13 Gain 349,671 60.6 5.3 55.3 9.21E−10 2.43E−07 USP25 chr31: 12,876,407-12,941,599 q13 Gain 65,192 57.6 5.3 52.3 4.74E−09 4.90E−07 chr31: 12,941,599-13,157,803 q13 Gain 216,204 57.6 6.6 51.0 1.92E−08 1.26E−06 LOC102154980 chr31: 13,157,803-13,956,866 q13 Gain 799,063 57.6 5.3 52.3 4.74E−09 4.90E−07 LOC102154980, LOC102155084 chr31: 13,956,866-14,419,403 q13 Gain 462,537 57.6 6.6 51.0 1.92E−08 1.26E−06 CXADR, LOC487695, LOC102155790, C31H21orf91 chr31: 14,419,403-14,964,747 q13 Gain 545,344 57.6 5.3 52.3 4.74E−09 4.90E−07 CHODL, TMPRSS15 chr31: 14,964,747-15,776,949 q13 Gain 812,202 60.6 5.3 55.3 9.21E−10 2.43E−07 chr31: 15,776,949-16,031,595 q13 Gain 254,646 57.6 3.9 53.6 9.72E−10 2.43E−07 chr31: 16,031,595-16,147,594 q13 Gain 115,999 60.6 3.9 56.7 1.79E−10 2.43E−07 chr31: 16,147,594-16,653,042 q13-q14 Gain 505,448 60.6 5.3 55.3 9.21E−10 2.43E−07 LOC102156052 chr31: 16,653,042-16,851,394 q14 Gain 198,352 63.6 5.3 58.4 1.67E−10 2.43E−07 chr31: 16,851,394-17,451,755 q14 Gain 600,361 60.6 5.3 55.3 9.21E−10 2.43E−07 EEF1A1, NCAM2 chr31: 17,451,755-17,748,133 q14 Gain 296,378 60.6 6.6 54.0 3.94E−09 4.90E−07 chr31: 17,748,133-17,766,239 q14 Gain 18,106 57.6 6.6 51.0 1.92E−08 1.26E−06 chr31: 17,766,239-18,277,334 q14 Gain 511,095 57.6 5.3 52.3 4.74E−09 4.90E−07 chr31: 19,317,125-19,482,884 q14 Gain 165,759 57.6 5.3 52.3 4.74E−09 4.90E−07 chr31: 19,889,449-19,909,607 q14 Gain 20,158 57.6 6.6 51.0 1.92E−08 1.26E−06 chr31: 19,909,607-20,085,761 q14 Gain 176,154 60.6 6.6 54.0 3.94E−09 4.90E−07 LOC102156356, LOC102156405 chr31: 20,085,761-20,166,178 q14 Gain 80,417 60.6 5.3 55.3 9.21E−10 2.43E−07 chr31: 20,166,178-20,180,593 q14 Gain 14,415 57.6 5.3 52.3 4.74E−09 4.90E−07 chr31: 20,869,268-21,005,369 q14 Gain 136,101 57.6 5.3 52.3 4.74E−09 4.90E−07 chr31: 21,005,369-21,031,105 q14 Gain 25,736 57.6 6.6 51.0 1.92E−08 1.26E−06 chr31: 21,031,105-21,257,672 q14 Gain 226,567 60.6 6.6 54.0 3.94E−09 4.90E−07 MRPL39, JAM2, ATP5J, GABPA, LOC102156977 chr31: 21,257,672-21,441,947 q14 Gain 184,275 57.6 6.6 51.0 1.92E−08 1.26E−06 APP chr31: 21,441,947-21,622,269 q14 Gain 180,322 60.6 6.6 54.0 3.94E−09 4.90E−07 APP, LOC102151217 chr31: 21,622,269-21,749,206 q14-q15.1 Gain 126,937 57.6 6.6 51.0 1.92E−08 1.26E−06 LOC102151217 chr31: 21,749,206-22,049,143 q15.1 Gain 299,937 57.6 5.3 52.3 4.74E−09 4.90E−07 LOC102151217, CYYR1, LOC487711 chr31: 22,049,143-22,759,280 q15.1 Gain 710,137 60.6 6.6 54.0 3.94E−09 4.90E−07 LOC102151271, ADAMTS1, ADAMTS5, LOC102151422 chr31: 22,759,280-23,102,209 q15.1 Gain 342,929 63.6 6.6 57.1 7.51E−10 2.43E−07 chr31: 23,102,209-23,622,081 q15.1 Gain 519,872 60.6 6.6 54.0 3.94E−09 4.90E−07 chr31: 23,622,081-23,967,926 q15.1 Gain 345,845 57.6 6.6 51.0 1.92E−08 1.26E−06 LOC102151634, LOC487715, LTN1 chr31: 23,967,926-24,163,030 q15.1 Gain 195,104 60.6 6.6 54.0 3.94E−09 4.90E−07 LTN1, RWDD2B, USP16, CCT8, C31H21orf7 chr31: 24,163,030-24,505,719 q15.1 Gain 342,689 60.6 5.3 55.3 9.21E−10 2.43E−07 C31H21orf7, BACH1, GRIK1 chr31: 24,505,719-24,642,006 q15.1 Gain 136,287 60.6 6.6 54.0 3.94E−09 4.90E−07 GRIK1 chr31: 24,642,006-25,422,064 q15.1 Gain 780,058 60.6 5.3 55.3 9.21E−10 2.43E−07 GRIK1, CLDN17, LOC102153451, CLDN8, KRTAP24-1, LOC102153645, KRTAP26-1, KRTAP27-1, KRTAP23-1, KRTAP13-2, LOC100686187, LOC100686265, LOC100686358, LOC100686441, LOC100686522, KRTAP19-4 chr31: 25,422,064-26,015,104 q15.1 Gain 593,040 60.6 6.6 54.0 3.94E−09 4.90E−07 LOC102151701, LOC102151782, LOC100686828, LOC102151849, KRTAP8-1, KRTAP7-1, KRTAP11-1 chr31: 26,015,104-26,207,846 q15.1 Gain 192,742 57.6 6.6 51.0 1.92E−08 1.26E−06 TIAM1 chr31: 26,366,005-26,510,397 q15.1 Gain 144,392 57.6 6.6 51.0 1.92E−08 1.26E−06 TIAM1 chr31: 26,510,397-26,548,528 q15.1 Gain 38,131 63.6 7.9 55.7 2.91E−09 4.90E−07 SOD1, SCAF4 chr31: 26,548,528-26,617,285 q15.1 Gain 68,757 63.6 6.6 57.1 7.51E−10 2.43E−07 SCAF4, LOC102155229 chr31: 26,617,285-26,647,666 q15.1 Gain 30,381 60.6 6.6 54.0 3.94E−09 4.90E−07 LOC102155229 chr31: 27,306,560-27,479,144 q15.1 Gain 172,584 60.6 6.6 54.0 3.94E−09 4.90E−07 SYNJ1, PAXBP1, C31H21orf62 chr31: 27,479,144-27,563,388 q15.1 Gain 84,244 57.6 6.6 51.0 1.92E−08 1.26E−06 C31H21orf62 chr31: 27,993,323-28,087,673 q15.1 Gain 94,350 66.7 14.5 52.2 1.36E−07 4.48E−06 LOC102151345, LOC100856276, LOC100856546 chr31: 28,087,673-28,150,222 q15.1 Gain 62,549 69.7 14.5 55.2 2.89E−08 1.48E−06 LOC100856546, LOC100856290, LOC100856570 chr31: 28,150,222-28,597,951 q15.1 Gain 447,729 66.7 6.6 60.1 1.33E−10 2.43E−07 LOC100856570, LOC100856585, DONSON, LOC100856647, LOC100856635, LOC102153983, ATP5O, LOC102152930 chr31: 28,597,951-28,648,317 q15.1 Gain 50,366 63.6 6.6 57.1 7.51E−10 2.43E−07 LOC100856716 chr31: 28,648,317-29,782,326 q15.1 Gain 1,134,009 60.6 6.6 54.0 3.94E−09 4.90E−07 IFNAR2, IL10RB, LOC102153360, LOC609830, LOC102153793, LOC487739, LOC478405, DNAJC28, LOC487740, SON, LOC478407, CRYZL1, ITSN1, LOC100688175, LOC100688250, LOC100855541, LOC102155085, SLC5A3, MRPS6, KCNE2, FAM165B chr31: 29,782,326-29,925,895 q15.1 Gain 143,569 66.7 10.5 56.1 6.34E−09 6.02E−07 FAM165B, LOC102155325, LOC102155418, LOC487743, RCAN1 chr31: 29,925,895-29,968,341 q15.1 Gain 42,446 63.6 10.5 53.1 3.13E−08 1.59E−06 RCAN1 chr31: 29,985,712-29,999,819 q15.1 Gain 14,107 60.6 9.2 51.4 4.78E−08 2.39E−06 RCAN1 chr31: 29,999,819-30,295,496 q15.1 Gain 295,677 60.6 6.6 54.0 3.94E−09 4.90E−07 RCAN1, CLIC6, RUNX1 chr31: 30,295,496-30,328,439 q15.1 Gain 32,943 63.6 6.6 57.1 7.51E−10 2.43E−07 RUNX1 chr31: 30,328,439-30,530,533 q15.1 Gain 202,094 60.6 6.6 54.0 3.94E−09 4.90E−07 RUNX1 chr31: 30,530,533-30,596,729 q15.1 Gain 66,196 63.6 6.6 57.1 7.51E−10 2.43E−07 chr31: 30,596,729-30,782,046 q15.1 Gain 185,317 66.7 6.6 60.1 1.33E−10 2.43E−07 LOC102156175 chr31: 30,782,046-30,975,922 q15.1 Gain 193,876 63.6 6.6 57.1 7.51E−10 2.43E−07 chr31: 30,975,922-31,201,087 q15.1 Gain 225,165 63.6 5.3 58.4 1.67E−10 2.43E−07 LOC609892 chr31: 31,201,087-31,227,785 q15.1 Gain 26,698 60.6 5.3 55.3 9.21E−10 2.43E−07 chr31: 31,227,785-31,294,473 q15.1 Gain 66,688 57.6 5.3 52.3 4.74E−09 4.90E−07 SETD4 chr31: 31,889,542-32,205,283 q15.1 Gain 315,741 60.6 6.6 54.0 3.94E−09 4.90E−07 SIM2, HLCS, DSCR6 chr31: 32,205,283-32,543,860 q15.1-q15.2 Gain 338,577 63.6 6.6 57.1 7.51E−10 2.43E−07 PIGP, TTC3, LOC102151700, DSCR3, DYRK1A chr31: 32,543,860-32,733,279 q15.2 Gain 189,419 57.6 6.6 51.0 1.92E−08 1.26E−06 DYRK1A, LOC102152474, LOC102151965 chr31: 34,066,143-34,101,861 q15.2 Gain 35,718 60.6 9.2 51.4 4.78E−08 2.39E−06 chr31: 34,101,861-34,121,248 q15.2 Gain 19,387 63.6 9.2 54.4 1.00E−08 9.41E−07 chr31: 34,121,248-34,151,499 q15.2 Gain 30,251 66.7 9.2 57.5 1.95E−09 4.59E−07 BRWD1 chr31: 34,151,499-34,325,320 q15.2 Gain 173,821 63.6 6.6 57.1 7.51E−10 2.43E−07 BRWD1, LOC100685290, HMGN1 chr31: 34,325,320-34,968,541 q15.2-q15.3 Gain 643,221 60.6 6.6 54.0 3.94E−09 4.90E−07 B3GALT5, IGSF5, PCP4, DSCAM chr31: 34,968,541-35,026,069 q15.3 Gain 57,528 57.6 6.6 51.0 1.92E−08 1.26E−06 DSCAM chr31: 35,212,014-35,436,330 q15.3 Gain 224,316 57.6 5.3 52.3 4.74E−09 4.90E−07 DSCAM chr31: 35,436,330-35,611,912 q15.3 Gain 175,582 57.6 6.6 51.0 1.92E−08 1.26E−06 chr31: 36,240,655-36,347,617 q15.3 Gain 106,962 54.5 3.9 50.6 4.94E−09 4.90E−07 PRDM15, LOC102154225, C2CD2 chr31: 39,613,234-39,674,420 q15.3 Gain 61,186 57.6 6.6 51.0 1.92E−08 1.26E−06 PCNT chr31: 39,674,420-39,702,857 q15.3 Gain 28,437 60.6 6.6 54.0 3.94E−09 4.90E−07 PCNT, DIP2A chr31: 39,702,857-39,895,921 q15.3 Gain 193,064 60.6 7.9 52.7 1.46E−08 1.19E−06 DIP2A, S100B, PRMT2 chr36:21,365,605-21,435,803 q14 Gain 70,198 54.5 2.6 51.9 8.40E−10 2.43E−07 LOC102152241, PDE11A chr36:21,435,803-21,571,604 q14 Gain 135,801 54.5 3.9 50.6 4.94E−09 4.90E−07 PDE11A chr36:22,206,259-22,262,382 q14 Gain 56,123 54.5 3.9 50.6 4.94E−09 4.90E−07 TTN chr36:22,262,382-22,880,297 q14 Gain 617,915 54.5 2.6 51.9 8.40E−10 2.43E−07 TTN, CCDC141, SESTD1 chr36:28,200,299-28,282,448 q15 Gain 82,149 60.6 9.2 51.4 4.78E−08 2.39E−06 FSIP2 chr36:28,282,448-28,298,114 q15 Gain 15,666 57.6 6.6 51.0 1.92E−08 1.26E−06 chr36:28,298,114-28,473,376 q15 Gain 175,262 54.5 2.6 51.9 8.40E−10 2.43E−07

TABLE 4 Significant CNAs in wt-KIT tumors identified by GISTIC analysis G- No. Region (CanFam3) Extended Region Type Q-Bound Score 1 chr1: 63,742,997-63,803,385 chr1: 63,742,997-63,803,385 Loss 9.40E−09 20.03 2 chr1: 98,513,040-98,728,698 chr1: 98,500,725-107,055,454 Gain 1.19E−02 4.09 3 chr1: 111,745,837-111,764,765 chr1: 111,745,837-111,773,264 Loss 1.15E−05 10.02 4 chr1: 117,108,373-117,174,987 chr1: 117,091,834-117,539,044 Gain 6.73E−08 8.39 5 chr2: 19,854,075-19,889,268 chr2: 19,854,075-19,897,270 Gain 3.42E−02 3.68 6 chr2: 23,201,419-23,241,899 chr2: 23,201,419-23,241,899 Loss 2.37E−05 9.32 7 chr2: 35,996,135-36,003,432 chr2: 35,921,044-36,003,432 Gain 1.16E−02 4.10 8 chr2: 84,126,143-84,151,550 chr2: 83,970,260-84,151,550 Gain 2.01E−10 14.74 9 chr2: 84,151,550-84,179,101 chr2: 84,151,550-84,179,101 Loss 3.72E−04 7.30 10 chr3: 59,944,361-60,185,123 chr3: 59,753,458-60,185,123 Gain 7.96E−05 6.04 11 chr3: 60,423,519-60,515,009 chr3: 60,423,519-60,585,098 Loss 7.40E−04 6.93 12 chr3: 91,351,987-91,448,431 chr3: 91,351,987-91,681,975 Loss 2.24E−02 5.09 13 chr3: 91,781,169-91,786,770 chr3: 91,681,975-91,786,770 Gain 8.21E−03 4.27 14 chr4: 14,010,773-14,124,513 chr4: 14,001,344-14,124,513 Gain 6.28E−03 4.36 15 chr4: 34,760,329-35,027,498 chr4: 34,745,245-35,056,833 Loss 9.70E−04 6.78 16 chr4: 35,861,977-36,002,900 chr4: 35,851,080-36,002,900 Gain 2.01E−10 15.55 17 chr5: 1-133,326 chr5: 1-133,326 Loss 6.97E−04 6.96 18 chr5: 19,772,292-19,870,693 chr5: 1,333,535-19,870,693 Gain 4.77E−03 4.48 19 chr5: 32,224,217-32,261,183 chr5: 32,224,217-32,312,981 Gain 2.01E−10 17.75 20 chr5: 56,349,012-56,415,940 chr5: 56,331,351-56,526,415 Gain 2.47E−03 4.78 21 chr5: 78,183,082-78,251,390 chr5: 78,150,222-78,399,717 Loss 9.40E−09 25.16 22 chr5: 78,251,390-78,342,207 chr5: 78,183,082-78,399,717 Gain 2.01E−10 13.74 23 chr5: 81,265,802-81,360,136 chr5: 81,265,802-81,360,136 Gain 2.35E−02 3.83 24 chr6: 9,019,278-9,130,821 chr6: 7,538,713-9,671,493 Gain 1.63E−07 8.11 25 chr6: 38,821,209-38,890,486 chr6: 38,821,209-38,967,548 Gain 2.01E−10 13.45 26 chr6: 40,670,034-40,690,650 chr6: 40,670,034-40,720,194 Loss 2.02E−07 12.14 27 chr6: 45,833,720-46,090,567 chr6: 45,426,941-46,481,846 Loss 1.82E−03 6.48 28 chr6: 47,044,166-47,111,711 chr6: 46,993,224-47,132,538 Gain 1.02E−04 5.96 29 chr7: 1-48,912 chr7: 1-74,145 Gain 3.10E−05 6.36 30 chr7: 41,776,035-41,851,194 chr7: 41,772,322-41,851,194 Gain 2.01E−10 14.09 31 chr7: 71,181,689-71,239,464 chr7: 71,181,689-71,239,464 Loss 9.40E−09 19.86 32 chr8: 2,739,349-2,765,393 chr8: 2,677,487-2,792,410 Gain 2.11E−10 10.79 33 chr8: 72,704,964-72,826,938 chr8: 72,677,095-73,022,906 Gain 1.31E−07 8.17 34 chr8: 73,379,979-73,400,408 chr8: 73,379,979-73,757,566 Loss 9.40E−09 39.12 35 chr9: 1-48,674 chr9: 1-48,674 Loss 1.33E−03 6.61 36 chr9: 503,870-532,310 chr9: 225,593-537,724 Gain 6.28E−05 6.14 37 chr9: 17,312,039-17,732,745 chr9: 17,312,039-17,969,293 Loss 2.93E−08 13.30 38 chr9: 17,969,293-18,092,618 chr9: 17,969,293-18,092,618 Gain 8.22E−08 8.33 39 chr9: 38,984,357-38,998,563 chr9: 38,972,929-38,998,563 Loss 9.40E−09 35.52 40 chr9: 38,998,563-39,010,776 chr9: 38,998,563-39,010,776 Gain 2.01E−10 13.90 41 chr9: 57,683,444-57,748,629 chr9: 57,683,444-61,074,082 Loss 2.81E−02 4.94 42 chr10: 1,728,923-1,848,374 chr10: 96,774-1,848,374 Gain 2.89E−06 7.18 43 chr10: 17,020,231-17,066,756 chr10: 17,020,231-17,066,756 Gain 2.01E−10 11.94 44 chr10: 17,775,058-17,872,494 chr10: 17,365,609-19,611,483 Loss 1.17E−03 6.69 45 chr10: 45,716,711-45,751,320 chr10: 45,691,620-45,803,956 Gain 9.85E−07 7.54 46 chr11: 52,587,841-52,683,204 chr11: 52,587,841-52,722,960 Gain 2.01E−10 19.38 47 chr11: 68,433,695-68,603,240 chr11: 68,433,695-68,603,240 Loss 1.53E−03 6.54 48 chr12: 1,529,469-1,583,900 chr12: 1,300,212-1,583,900 Gain 2.26E−04 5.68 49 chr12: 2,604,766-2,661,228 chr12: 2,604,766-2,664,169 Gain 2.01E−10 17.59 50 chr12: 72,079,628-72,251,175 chr12: 72,079,628-72,272,418 Loss 4.57E−05 8.64 51 chr13: 2,443,805-2,490,453 chr13: 2,443,805-2,490,453 Loss 9.40E−09 15.41 52 chr13: 35,964,725-36,122,185 chr13: 35,964,725-36,149,644 Loss 9.17E−05 8.15 53 chr13: 37,377,311-37,700,001 chr13: 37,364,026-37,746,957 Gain 2.01E−10 12.93 54 chr14: 2,808,378-2,842,648 chr14: 2,113,422-2,861,174 Loss 9.40E−09 17.21 55 chr14: 4,972,455-5,338,685 chr14: 4,938,913-5,338,685 Gain 2.81E−03 4.73 56 chr14: 43,551,303-43,621,522 chr14: 43,537,923-43,629,021 Gain 2.01E−10 14.74 57 chr15: 1,346,848-1,499,163 chr15: 1,346,848-1,499,163 Gain 5.96E−10 9.91 58 chr15: 7,819,131-7,934,777 chr15: 5,036,356-7,980,691 Loss 6.24E−03 5.85 59 chr15: 63,811,147-63,982,204 chr15: 63,811,147-63,982,204 Loss 1.05E−04 8.05 60 chr16: 1-309,348 chr16: 1-407,916 Loss 2.09E−03 6.39 61 chr16: 9,149,776-9,181,393 chr16: 8,551,949-9,217,088 Gain 1.32E−02 4.04 62 chr16: 54,356,690-54,473,666 chr16: 54,297,462-56,683,206 Loss 1.46E−05 9.80 63 chr17: 403,827-620,898 chr17: 1-689,496 Loss 4.31E−05 8.68 64 chr17: 56,665,179-56,686,048 chr17: 56,665,179-56,686,048 Gain 5.96E−09 9.13 65 chr17: 64,159,048-64,289,059 chr17: 64,159,048-64,289,059 Loss 6.44E−03 5.82 66 chr18: 1,976,678-2,065,321 chr18: 1-2,065,321 Loss 1.67E−02 5.28 67 chr18: 11,339,197-11,416,499 chr18: 11,339,197-11,416,499 Gain 2.94E−05 6.39 68 chr18: 38,683,599-38,800,287 chr18: 38,683,599-38,800,287 Loss 1.55E−03 6.53 69 chr18: 46,318,953-46,383,384 chr18: 46,318,953-46,510,748 Gain 2.01E−10 12.96 70 chr19: 2,901,106-2,979,475 chr19: 2,901,106-2,979,475 Gain 6.67E−05 6.11 71 chr19: 20,036,378-20,295,719 chr19: 19,933,994-20,329,284 Loss 2.84E−04 7.45 72 chr20: 15,968,550-16,176,199 chr20: 15,198,750-16,176,199 Loss 2.18E−04 7.63 73 chr20: 38,713,828-38,743,160 chr20: 38,311,784-38,774,596 Loss 3.78E−07 11.95 74 chr20: 40,469,449-40,547,257 chr20: 40,469,449-40,551,201 Gain 1.51E−02 4.00 75 chr20: 46,131,350-46,186,781 chr20: 45,703,872-46,356,123 Gain 2.01E−10 19.43 76 chr20: 53,267,512-53,327,296 chr20: 53,267,512-53,327,296 Loss 9.40E−09 45.38 77 chr20: 53,267,512-53,341,759 chr20: 53,267,512-53,341,759 Gain 2.74E−03 4.73 78 chr20: 54,212,601-54,231,471 chr20: 54,201,255-54,231,471 Gain 1.53E−08 8.84 79 chr20: 57,137,484-57,189,778 chr20: 54,861,893-58,134,056 Loss 2.00E−02 5.16 80 chr21: 21,566,631-21,679,745 chr21: 21,566,631-21,695,604 Gain 2.01E−10 13.61 81 chr22: 780,737-924,571 chr22: 242,796-924,571 Loss 1.61E−03 6.52 82 chr22: 60,344,869-60,562,889 chr22: 60,297,547-60,633,852 Loss 3.82E−02 4.71 83 chr23: 8,242,315-8,331,657 chr23: 8,224,915-8,331,657 Gain 3.30E−03 4.66 84 chr23: 20,504,112-20,514,633 chr23: 20,504,112-20,514,633 Loss 9.40E−09 25.27 85 chr23: 20,538,312-20,710,885 chr23: 20,514,633-20,710,885 Gain 2.01E−10 14.20 86 chr23: 52,151,945-52,294,480 chr23: 52,151,945-52,294,480 Loss 2.33E−04 7.57 87 chr24: 21,138,869-21,154,133 chr24: 21,138,869-21,213,663 Gain 2.01E−10 13.32 88 chr24: 47,449,078-47,698,779 chr24: 47,449,078-47,698,779 Loss 5.03E−03 5.95 89 chr25: 50,413,480-51,073,874 chr25: 50,413,480-51,184,426 Gain 1.99E−05 6.53 90 chr25: 51,475,875-51,628,933 chr25: 51,465,331-51,628,933 Loss 1.50E−04 7.82 91 chr26: 20,263,200-20,298,649 chr26: 20,209,073-20,298,649 Gain 7.21E−08 8.37 92 chr26: 25,221,909-25,467,475 chr26: 24,951,165-25,676,067 Loss 1.13E−03 6.71 93 chr26: 27,167,936-27,174,566 chr26: 27,167,936-27,174,566 Gain 2.01E−10 31.38 94 chr26: 27,174,566-27,207,467 chr26: 27,174,566-27,240,032 Loss 9.40E−09 66.15 95 chr26: 27,289,594-27,333,602 chr26: 27,289,594-27,373,928 Gain 3.56E−02 3.62 96 chr26: 27,462,452-27,483,067 chr26: 27,462,452-27,569,156 Loss 2.50E−03 6.30 97 chr26: 28,340,781-28,398,606 chr26: 28,324,491-28,456,230 Gain 8.04E−09 9.06 98 chr27: 1,114,154-1,303,187 chr27: 1-1,314,884 Loss 7.25E−05 8.35 99 chr27: 2,901,529-3,020,954 chr27: 2,876,411-3,020,954 Gain 1.39E−02 4.03 100 chr27: 6,978,563-7,023,641 chr27: 6,954,304-7,041,668 Gain 2.01E−10 18.06 101 chr27: 25,750,959-25,860,828 chr27: 25,732,852-25,860,828 Loss 9.40E−09 15.89 102 chr27: 31,853,354-31,912,150 chr27: 25,889,191-38,183,454 Loss 6.24E−03 5.85 103 chr27: 38,183,454-38,208,381 chr27: 38,183,454-42,266,862 Gain 9.02E−03 4.23 104 chr28: 29,626,077-29,713,479 chr28: 29,626,077-29,713,479 Gain 2.87E−02 3.75 105 chr28: 40,520,701-40,593,303 chr28: 40,520,701-40,950,687 Gain 1.70E−09 9.52 106 chr28: 41,109,678-41,137,968 chr28: 41,109,678-41,182,112 Loss 8.95E−07 11.34 107 chr29: 41,021,811-41,067,224 chr29: 41,021,811-41,845,238 Loss 7.67E−03 5.72 108 chr30: 37,840,545-37,864,405 chr30: 37,810,533-37,864,405 Gain 1.83E−05 6.55 109 chr30: 38,521,916-38,804,831 chr30: 38,521,916-38,816,298 Loss 9.95E−03 5.58 110 chr31: 35,871,715-35,887,990 chr31: 35,781,416-36,613,542 Loss 4.20E−03 6.04 111 chr31: 37,299,916-37,327,631 chr31: 36,664,322-38,941,533 Gain 6.60E−10 9.86 112 chr33: 30,728,795-30,797,016 chr33: 30,659,185-31,185,184 Gain 1.90E−02 3.90 113 chr34: 11,275,814-11,298,924 chr34: 11,275,814-11,315,700 Gain 3.28E−10 10.20 114 chr34: 11,470,898-11,501,692 chr34: 11,338,716-11,552,092 Loss 1.58E−03 6.53 115 chr35: 16,974,084-16,984,185 chr35: 16,912,748-17,010,530 Gain 1.39E−02 4.03 116 chr36: 28,176,666-28,282,448 chr36: 28,176,666-28,282,448 Gain 3.63E−02 3.58 117 chr37: 1-177,983 chr37: 1-177,983 Loss 2.77E−05 9.15 118 chr37: 177,983-248,456 chr37: 177,983-266,890 Gain 2.81E−03 4.72 119 chr37: 24,943,587-25,049,058 chr37: 24,943,587-25,049,058 Gain 2.01E−10 12.26 120 chr37: 30,389,938-30,452,090 chr37: 30,312,057-30,837,612 Loss 2.70E−04 7.47 121 chr37: 30,893,538-30,902,991 chr37: 30,893,538-30,902,991 Gain 5.00E−03 4.46 122 chr38: 724,265-752,803 chr38: 717,842-752,803 Gain 4.17E−02 3.53 123 chr38: 22,131,188-22,290,537 chr38: 22,131,188-23,707,309 Gain 1.94E−05 6.53 124 chr38: 23,762,444-23,914,537 chr38: 23,762,444-23,914,537 Loss 4.09E−02 4.66 125 chrX: 1-93,751 chrX: 1-93,751 Gain 5.57E−10 9.95 126 chrX: 32,269,966-32,297,891 chrX: 32,269,966-32,297,891 Loss 9.40E−09 19.94 127 chrX: 53,176,671-53,453,526 chrX: 53,139,071-53,453,526 Gain 3.05E−04 5.56 128 chrX: 71,803,071-71,951,577 chrX: 71,744,008-72,049,231 Loss 9.40E−09 68.76 129 chrX: 72,228,723-72,239,538 chrX: 72,228,723-72,239,538 Gain 2.01E−10 16.15 130 chrX: 120,981,052-121,071,550 chrX: 120,943,561-121,071,550 Loss 1.68E−05 9.64 131 chrX: 121,545,250-121,743,104 chrX: 121,545,250-122,322,644 Gain 1.98E−07 8.06

TABLE 5 Significant CNAs in mut-KIT tumors identified by GISTIC analysis G- No. Region Extended Region Type Q-Bound Score 1 chr1: 111,745,837-111,773,264 chr1: 108,222,732-111,971,009 Gain 6.26E−03 5.11 2 chr1: 111,745,837-111,773,264 chr1: 111,745,837-111,773,264 Loss 6.27E−03 5.47 3 chr2: 23,201,419-23,241,899 chr2: 23,201,419-23,241,899 Loss 1.22E−03 6.32 4 chr2: 35,921,044-35,970,446 chr2: 35,903,106-35,996,135 Gain 6.62E−04 6.45 5 chr2: 84,151,550-84,179,101 chr2: 84,151,550-84,179,101 Loss 1.68E−02 4.64 6 chr4: 35,861,977-35,979,387 chr4: 35,842,040-36,002,900 Gain 1.94E−05 7.72 7 chr5: 17,003,811-17,271,198 chr5: 9,062,538-20,133,725 Loss 2.99E−04 6.95 8 chr5: 78,150,222-78,183,082 chr5: 78,150,222-78,415,957 Loss 1.34E−09 21.52 9 chr5: 78,415,957-78,459,732 chr5: 78,415,957-78,459,732 Gain 4.49E−03 5.46 10 chr5: 82,845,474-83,027,347 chr5: 81,360,136-88,915,250 Loss 6.36E−03 5.46 11 chr6: 40,670,034-40,712,665 chr6: 40,656,427-40,712,665 Loss 2.70E−06 9.90 12 chr6: 47,044,166-47,111,711 chr6: 47,025,866-47,111,711 Gain 1.02E−03 6.25 13 chr7: 41,818,563-41,843,764 chr7: 41,591,428-41,851,194 Gain 3.40E−03 5.63 14 chr7: 71,181,689-71,239,464 chr7: 42,534,961-80,974,532 Loss 1.58E−02 4.70 15 chr8: 73,379,979-73,391,779 chr8: 73,379,979-74,008,358 Loss 1.34E−09 19.31 16 chr9: 0-40,344 chr9: 1-212,867 Loss 5.87E−03 5.50 17 chr9: 17,297,936-17,312,039 chr9: 17,259,902-17,312,039 Gain 9.13E−06 7.95 18 chr9: 38,972,929-38,998,563 chr9: 38,972,929-38,998,563 Loss 1.34E−09 26.83 19 chr9: 38,998,563-39,010,776 chr9: 38,998,563-39,010,776 Gain 1.22E−03 6.16 20 chr10: 17,365,609-17,503,037 chr10: 17,365,609-18,606,267 Loss 3.17E−02 4.17 21 chr11: 52,587,841-52,700,545 chr11: 52,587,841-52,722,960 Gain 6.01E−03 5.18 22 chr11: 68,461,968-68,603,240 chr11: 68,433,695-68,612,918 Loss 1.24E−03 6.31 23 chr12: 2,604,766-2,643,750 chr12: 2,604,766-2,661,228 Gain 1.14E−02 4.48 24 chr12: 72,079,628-72,251,175 chr12: 72,017,711-72,328,821 Loss 1.63E−02 4.67 25 chr13: 2,443,805-2,490,453 chr13: 2,443,805-2,490,453 Loss 2.01E−05 8.25 26 chr13: 46,940,621-47,030,653 chr13: 43,553,562-62,751,630 Gain 2.51E−05 7.64 27 chr14: 2,522,789-2,842,648 chr14: 2,322,259-2,861,174 Loss 1.68E−06 10.43 28 chr14: 18,158,442-18,470,051 chr14: 16,268,322-18,687,430 Gain 6.54E−07 8.71 29 chr15: 7,644,414-7,992,325 chr15: 7,042,368-7,992,325 Loss 4.00E−02 4.01 30 chr15: 63,811,147-63,951,198 chr15: 63,773,273-63,982,204 Loss 2.79E−03 5.94 31 chr16: 54,356,690-54,473,666 chr16: 54,356,690-54,530,683 Loss 2.70E−06 9.90 32 chr17: 403,827-620,898 chr17: 1-1,194,858 Loss 1.57E−02 4.71 33 chr17: 56,787,475-56,820,514 chr17: 56,665,179-56,855,694 Gain 3.27E−03 5.66 34 chr17: 64,174,093-64,289,059 chr17: 64,159,048-64,289,059 Loss 4.10E−03 5.72 35 chr18: 38,726,595-38,789,728 chr18: 38,655,067-38,800,287 Loss 9.03E−05 7.46 36 chr19: 20,036,378-20,295,719 chr19: 19,933,994-20,329,284 Loss 4.10E−03 5.72 37 chr20: 48,403,168-48,458,171 chr20: 48,053,474-48,512,348 Gain 1.78E−04 6.97 38 chr20: 53,267,512-53,327,296 chr20: 53,267,512-53,327,296 Loss 8.57E−07 11.07 39 chr21: 45,003,472-45,130,560 chr21: 43,197,918-50,858,623 Gain 4.69E−02 3.56 40 chr22: 60,297,547-60,513,761 chr22: 60,297,547-61,439,934 Loss 1.03E−03 6.41 41 chr23: 20,504,112-20,669,834 chr23: 20,504,112-20,710,885 Loss 1.34E−09 15.18 42 chr23: 52,151,945-52,294,480 chr23: 52,151,945-52,294,480 Loss 6.51E−03 5.43 43 chr24: 47,470,936-47,698,779 chr24: 44,594,747-47,698,779 Loss 1.07E−02 5.02 44 chr25: 49,654,288-49,893,248 chr25: 49,161,012-51,246,100 Loss 2.73E−02 4.27 45 chr26: 5,372,761-5,454,082 chr26: 5,021,666-8,071,151 Gain 1.47E−03 6.08 46 chr26: 27,167,936-27,174,566 chr26: 27,167,936-27,174,566 Gain 4.29E−15 16.35 47 chr26: 27,174,566-27,207,467 chr26: 27,174,566-27,240,032 Loss 1.34E−09 38.88 48 chr27: 6,978,563-7,041,668 chr27: 6,954,304-7,041,668 Gain 1.04E−02 4.58 49 chr28: 39,142,525-39,236,726 chr28: 36,947,617-40,499,923 Loss 3.96E−06 9.32 50 chr29: 40,988,530-41,067,224 chr29: 40,654,989-41,845,238 Loss 3.12E−02 4.17 51 chr31: 28,109,184-28,150,222 chr31: 20,869,268-35,611,912 Gain 4.29E−15 13.43 52 chr32: 1,665,965-2,024,718 chr32: 1-2,989,645 Loss 5.58E−03 5.54 53 chr33: 14,686,038-14,789,970 chr33: 1,117,447-23,505,256 Loss 9.49E−04 6.45 54 chr36: 19,878,982-19,955,258 chr36: 6,988,336-23,357,555 Gain 2.17E−06 8.36 55 chr37: 24,954,721-24,972,543 chr37: 24,943,587-25,049,058 Gain 2.08E−02 4.01 56 chr37: 30,312,057-30,452,090 chr37: 30,312,057-30,902,991 Loss 5.22E−03 5.59 57 chr38: 1,056,312-1,123,624 chr38: 697,930-1,163,327 Gain 4.64E−04 6.60 58 chrX: 407,553-427,889 chrX: 1-427,889 Loss 4.07E−02 3.99 59 chrX: 32,269,966-32,297,891 chrX: 32,202,589-32,328,959 Loss 9.28E−04 6.46 60 chrX: 71,762,610-71,780,002 chrX: 71,744,008-72,105,721 Gain 8.83E−06 7.95 61 chrX: 71,780,002-72,216,345 chrX: 71,744,008-72,228,723 Loss 1.34E−09 27.09 62 chrX: 120,981,052-121,071,550 chrX: 120,908,381-121,071,550 Loss 7.22E−03 5.38

TABLE 6 CNV to differentiate wt-KIT high grade tumors vs wt-KIT low-grade tumors wt-KIT high freq wt-KIT low freq Region Event Length (%) (%) Gene Symbols chr20: 31,578,505- CN 155,618 77.3 5.6 LOC100685571, 31,734,123 Loss C20H3orf67 chr20: 46,389,048- CN 75,061 81.8 9.3 OR10A11, 46,464,109 Gain OR18C09, LOC102155605 CNV to differentiate Mut-KIT tumors vs wt-KIT low-grade tumors Region Mut-KIT freq wt-KIT low freq Region Event Length (%) (%) Gene Symbols chr5: 37,788,552- CN 178,293 75.8 0 COX 10 37,966,845 Loss chr31: 16,653,042- CN 198,352 63.6 0 16,851,394 Gain

8. SEQUENCE LISTING

Incorporation-by-Reference of Material Submitted Electronically

This application contains a sequence listing. It has been submitted electronically via EFS-Web as an ASCII text file entitled “127-97-PCT_2017-05-26_SEQ_ST25.txt”. The sequence listing is 5322 bytes in size, and was created on May 26, 2017. It is hereby incorporated by reference in its entirety.

It should be understood that the above description is only representative of illustrative embodiments and examples. For the convenience of the reader, the above description has focused on a limited number of representative examples of all possible embodiments, examples that teach the principles of the disclosure. The description has not attempted to exhaustively enumerate all possible variations or even combinations of those variations described. That alternate embodiments may not have been presented for a specific portion of the disclosure, or that further undescribed alternate embodiments may be available for a portion, is not to be considered a disclaimer of those alternate embodiments. One of ordinary skill will appreciate that many of those undescribed embodiments, involve differences in technology and materials rather than differences in the application of the principles of the disclosure. Accordingly, the disclosure is not intended to be limited to less than the scope set forth in the following claims and equivalents.

INCORPORATION BY REFERENCE

All references, articles, publications, patents, patent publications, and patent applications cited herein are incorporated by reference in their entireties for all purposes. However, mention of any reference, article, publication, patent, patent publication, and patent application cited herein is not, and should not be taken as an acknowledgment or any form of suggestion that they constitute valid prior art or form part of the common general knowledge in any country in the world. It is to be understood that, while the disclosure has been described in conjunction with the detailed description, thereof, the foregoing description is intended to illustrate and not limit the scope. Other aspects, advantages, and modifications are within the scope of the claims set forth below. All publications, patents, and patent applications cited in this specification are herein incorporated by reference as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. 

What is claimed is:
 1. A method for detecting a high-risk phenotype of a mast cell tumor (MCT) in a biological sample from a dog which comprises: (a) measuring copy numbers of regions of CFA5:38, CFA 20:32, CFA 20:46, and CFA 31:18 in the biological sample; (b) comparing the measured copy numbers to those of appropriate canine normal controls; and (c) if the copy numbers of regions of and CFA 20:46 and CFA 31:18 are increased and the copy numbers of regions of CFA5:38 and CFA 20:32 are reduced from that of the appropriate controls, detecting that the dog has increased likelihood of a high-risk phenotype of a mast cell tumor (MCT).
 2. The method of claim 1, wherein the copy number increases are >1.5.
 3. The method of claim 1, wherein the copy number reductions are <0.5
 4. The method of claim 1, wherein both the copy number increases are >1.5 and the copy number reductions are <0.5.
 5. The method of claim 1, wherein the copy numbers are measured by fluorescence in situ hybridization (FISH).
 6. The method of claim 1, wherein the copy numbers are measured by polymerase chain reaction (PCR).
 7. The method of claim 6, wherein the PCR is a digital droplet PCR
 8. The method of claim 1, wherein the copy numbers are measured by comparative genomic hybridization (CGH).
 9. The method of claim 1, wherein the copy numbers are measured by next generation sequencing.
 10. The method of claim 1, wherein the biological sample is a tissue sample.
 11. The method of claim 1, wherein the sample is a fresh-frozen sample.
 12. The method of claim 1, wherein the sample is a fresh sample.
 13. The method of claim 1, wherein the sample is a fixed sample.
 14. The method of claim 13, wherein the fixed sample is a formalin-fixed, paraffin-embedded sample.
 15. A kit for detecting a high-risk phenotype of a mast cell tumor (MCT) in a biological sample in a dog comprising: (a) at least a plurality of reagents selected from the group consisting of: nucleic acid probes and/or primers capable of specifically detecting CFA5:38, CFA 31:18, CFA 20:32, and CFA 20:46; and (b) instructions for use in measuring a copy number of regions of CFA5:38, CFA 20:32, CFA 20:46, and 31:18 in a biological sample from a dog, wherein if the copy numbers of regions of CFA 20:46 and CFA 31:18 are increased and the copy numbers of regions of CFA5:38 and CFA 20:32 are reduced from that of measured copy numbers for appropriate controls, detecting that the dog has increased likelihood of a high-risk phenotype of a mast cell tumor (MCT).
 16. The kit of claim 15, wherein the reagents comprise primers with SEQ ID NOS:4-7 and 11-14 and probes with SEQ ID NOS:15-18.
 17. A method for treating a dog with a mast cell tumor (MCT) which comprises: (a) measuring copy numbers of regions of CFA5:38, CFA 20:32, and CFA 20:46, and CFA 31:18 in a biological sample from the dog; (b) comparing the measured copy numbers to those of appropriate canine normal controls; (c) if the copy numbers of regions of CFA 20:46 and CFA 31:18 are increased and the copy numbers of regions of CFA5:38 and CFA 20:32 are reduced from that of the appropriate controls, detecting that the dog has increased likelihood of a high-risk phenotype of a mast cell tumor (MCT); and (d) treating the dog with a chemotherapy regimen.
 18. The method of treatment of claim 18, wherein the chemotherapy regimen comprises a treatment with an alkylating agent, a tyrosine kinase inhibitor, a vinca alkaloid or a combination thereof.
 19. The method of claim 18, wherein the alkylating agent is a nitrosourea.
 20. The method of claim 18, wherein the tyrosine kinase inhibitor is toceranib, masatinib, or imatinib.
 21. The method of claim 18, wherein the vinca alkyloid is vinblastine. 